Surveillance system and method having an operating mode partitioned fault classification model

ABSTRACT

A system and method which partitions a parameter estimation model, a fault detection model, and a fault classification model for a process surveillance scheme into two or more coordinated submodels together providing improved diagnostic decision making for at least one determined operating mode of an asset.

CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application is a continuation in part patent application ofU.S. patent application Ser. No. 09/591,140, filed Jun. 9, 2000,currently pending,

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

[0002] The invention described herein was made in the performance ofwork under NASA Small Business Innovation Research (SBIR) ContractsNAS4-99012 and NAS13-01001, and is subject to the provisions of PublicLaw 96-517 (35 USC 202) and the Code of Federal Regulations 48 CFR52.227-11 as modified by 48 CFR 1852.227-11, in which the contractor haselected to retain title.

FIELD OF THE INVENTION

[0003] The instant invention relates generally to a system and methodfor fault classification using operating mode partitioning and, inparticular, to a system and method for performing high sensitivitysurveillance of an asset such as a process and/or apparatus preferablyhaving at least two distinct modes of operation wherein surveillance isperformed using an operating mode partitioned fault classification modelof the asset.

BACKGROUND OF THE INVENTION

[0004] Conventional process surveillance schemes are sensitive only togross changes in the mean value of a process signal or to large steps orspikes that exceed some threshold limit value. These conventionalmethods suffer from either a large number of false alarms (if thresholdsare set too close to normal operating levels) or from a large number ofmissed (or delayed) alarms (if the thresholds are set too expansively).Moreover, most conventional methods cannot perceive the onset of aprocess disturbance or sensor signal error that gives rise to a signalbelow the threshold level or an alarm condition. Most conventionalmethods also do not account for the relationship between a measurementby one sensor relative to another sensor. Further, most conventionalmethods provide no means to assess the most likely cause of a processdisturbance or sensor signal error. For example, a process disturbancecould result from any combination of an instrumentation problem, anequipment problem, or the process operating in a new or unexpected way.

[0005] Recently, improved methods for process surveillance havedeveloped from the application of certain aspects of artificialintelligence technology. Specifically, parameter estimation methods havebeen developed using either statistical, mathematical or neural networktechniques to learn a model of the normal patterns present in a systemof process signals. After learning these patterns, the learned model isused as a parameter estimator to create one or more virtual signalsgiven a new observation of the actual process signals. Further, highsensitivity surveillance methods have been developed for detectingprocess and signal faults by analysis of a mathematical comparisonbetween the actual process signal and its virtual signal counterpart.Moreover, automated decision making methods have been developed forreasoning about the cause of events or problems on the basis of theirsymptoms as represented in data.

[0006] Parameter estimation based surveillance schemes have been shownto provide improved surveillance relative to conventional schemes for awide variety of assets including industrial, utility, business, medical,transportation, financial, and biological systems. However, parameterestimation based surveillance schemes have in general shown limitedsuccess when applied to complex processes. Applicant recognizes andbelieves that this is because the parameter estimation model for acomplex process must characterize the entire operating state space ofthe process to provide effective surveillance. Moreover, a review of theknown prior-art discloses that virtually all such systems developed todate utilize a single model of the process to span the entire set ofpossible operating modes. Hence, a significant shortcoming of the knownprior-art is that, inter alia, statistically derived models becomeextremely large and neural network models become difficult orimpractical to train when the process operating state space is complex.The implication for statistically derived models is that the parameterestimation method and system becomes computationally expensive tooperate thereby limiting the utility of the method for on-line orreal-time surveillance. An alternative for statistically derived modelsis to constrain the size of the model; however this constraint limitsthe accuracy of the parameter estimation method and thereby limits thesensitivity of the surveillance method. The implication for mathematicaland neural network models is simply that the parameter estimation methodand system becomes less accurate thereby degrading the sensitivity ofthe surveillance method. These shortcomings in parameter estimation andthe dependent capability for fault detection also reduce the utility,performance and benefit of automated decision making methods. Further,automated decision making itself becomes a much more complex and lessreliable procedure when the process operating state space is complex.Automated decision making when the process operating state space iscomplex often leads to conflicting and incompatible decision objectivesand fault patterns when considering multiple operating modes of theprocess. In fact, automated decision making when the process operatingstate space is complex can become combinatorially infeasible toaccomplish with the reliability and confidence needed for practical use.

[0007] Many attempts to apply parameter estimation, fault detection, andfault classification techniques to assets such as industrial, utility,business, medical, transportation, financial, and biological processeshave met with poor results in part because the techniques used wereexpected to characterize the entire operating state space of theprocess. In one example, a multivariate state estimation technique(MSET) based surveillance system for the Space Shuttle Main Engine'stelemetry data was found to produce numerous false alarms when thelearned MSET parameter estimation model was constrained to a sizesuitable for on-line, real-time surveillance. In this case, thesurveillance system false alarm rate could be reduced by desensitizingthe surveillance threshold parameters; however, the missed alarm ratesthen became too high for practical use in the telemetry data monitoringapplication. In another case, a Bayesian belief network faultclassification system for the X-33 Single Stage to Orbit Demonstratorvehicle was found to classify fault indications incorrectly whenmultiple operating modes of the system were represented in a singledecision model.

[0008] Moreover, current parameter estimation, fault detection, andfault classification techniques for surveillance of assets such asindustrial, utility, business, medical, transportation, financial, andbiological processes fail to recognize the surveillance performancelimitations that occur when it becomes necessary to trade-off decisionprocessing speed against decision accuracy. This may be attributed, inpart, to the relative immaturity of the field of artificial intelligenceand computer-assisted surveillance with regard to real-world processcontrol applications. Additionally, a general failure to recognize thespecific limitations of trading off decision processing speed againstdecision accuracy for computer-assisted surveillance is punctuated by anapparent lack of known prior art teachings that address potentialmethods to overcome this limitation. In general, the known prior-artteaches computer-assisted surveillance solutions that are either appliedglobally to all operating modes of an asset or applied only to a singlepredominant operating mode, for example, applied only to steady stateoperations while neglecting all transient operating states of the asset.

[0009] For the foregoing reasons, there is a need for a surveillancesystem and method that overcomes the significant shortcoming of theknown prior-art as delineated hereinabove.

BRIEF SUMMARY OF THE INVENTION

[0010] The instant invention is distinguished over the known prior artin a multiplicity of ways. For one thing, one embodiment of theinvention provides a surveillance system and method that partitionsdecision models of an asset for overcoming a performance limitingtrade-off between decision processing speed and decision accuracy thathas been generally unrecognized by the known prior art. Additionally,one embodiment of the invention can employ any one of a plurality ofparameter estimation methods, fault detection methods, and faultclassification methods and the decision models used therewith forimproving surveillance performance. Furthermore, one embodiment of theinvention provides a surveillance system and method that provides anoperating mode partitioned decision model that can be accomplished byobservation and analysis of a time sequence of process signal data andby a combination of a plurality of techniques.

[0011] Moreover, one embodiment of the invention provides a surveillancesystem and method that provides an operating mode partitioning of thedecision model which enables different parameter estimation methods,fault detection methods, and fault classification methods to be used forsurveillance within each individual operating mode of an asset. Thisability enables surveillance to be performed by the instant inventionwith lower false alarm rates and lower missed alarm rates than can beachieved by the known prior-art methods.

[0012] Hence, one embodiment of the invention provides a surveillancesystem and method that performs its intended function much moreeffectively by enabling higher decision processing speed without aconcomitant reduction in decision accuracy. Conversely, one embodimentof the invention alternately enables improved decision accuracy withouta concomitant reduction in decision processing speed. Additionally,these competing criteria may be traded-off to achieve the optimalperformance solution for a specific surveillance application.Furthermore, and in contrast to the known prior art, and in oneembodiment of the invention, parameter estimation methods, faultdetection methods, and fault classification methods may be individuallytailored for each operating mode of the asset thereby providingadditional capability to reduce decision error rates for thesurveillance system.

[0013] In one embodiment of the invention, the instant inventionprovides a surveillance system and method that creates and uses, for thepurpose of asset surveillance, a coordinated collection of decisionsubmodels wherein each decision submodel in the coordinated collectionis optimized for a single operating mode or subset of operating modes ofan asset.

[0014] In another embodiment of the invention, an asset surveillancesystem is comprised of an operating mode partitioned faultclassification model of an asset comprised of a plurality of faultclassification submodels each having an asset operating mode associatedthereto; a fault indication means for determining one or more faultindications given a set of observed asset signals from the asset; meansfor determining at least one operating mode of the asset for the set ofobserved asset signals; a first selection means for selecting at leastone of the fault classification submodels from the operating modepartitioned fault classification model as a function of at least the onedetermined operating mode for providing a fault classification ofdetermined fault indications for performing asset surveillance. Thefault indication means further includes an operating mode partitionedparameter estimation model comprised of a plurality of parameterestimation submodels each having an asset operating mode associatedthereto and a second selection means for selecting at least one of theparameter estimation submodels from the operating mode partitionedparameter estimation model as a function of at least the one determinedoperating mode. The fault indication means further includes means forprocessing the observed asset signals as a function of at least the oneselected parameter estimation submodel for defining parameter estimateddata. Additionally, the fault indication means includes an operatingmode partitioned fault detection model comprised of a plurality of faultdetection submodels each having an asset operating mode associatedthereto. Furthermore, the fault indication means further includes athird selection means for selecting at least one of the fault detectionsubmodels from the operating mode partitioned fault detection model as afunction of at least the one determined operating mode. Moreover, thefault indication means further includes means for processing theparameter estimated data as a function of at least the one selectedfault detection submodel for determining the one or more faultindications used for providing the fault classification of determinedfault indications by the first selection means selecting at least one ofthe fault classification submodels from the operating mode partitionedfault classification model as a function of both the one or more faultindications and at least the one determined operating mode for providingthe fault classification of determined fault indications for performingasset surveillance.

[0015] In another embodiment of the invention, a method for determiningasset status includes the steps of creating an operating modepartitioned fault classification model comprised of a plurality of faultclassification submodels each having an asset operating mode associatedthereto; acquiring a set of observed signal data values from an asset;determining at least one fault indication as a function of the observedsignal data values; determining at least one operating mode of the assetfor the set of observed asset signals; selecting at least one faultclassification submodel from the operating mode partitioned faultclassification model as a function of at least the one determinedoperating mode, and using at least the one fault indication and at leastthe one selected fault classification submodel for classifying faultsfor performing asset surveillance.

[0016] In another embodiment of the invention, a method for determiningasset status includes the steps of partitioning a decision model into aplurality of partitions, each partition having an operating modeassociated thereto: employing a plurality of different methods from aplurality of parameter estimation methods, a plurality of faultdetection methods, and a plurality of fault classification methods fordifferent partitions; determining at least one operating mode of anasset; selecting at least one the plurality of partitions as a functionof the determined operating mode for tailoring the plurality ofparameter estimation methods, the plurality of fault detection methods,and the plurality of fault classification methods to perform assetsurveillance as a function of the at least one determined operatingmode.

[0017] In another embodiment of the invention, a method for determiningasset status includes the steps of acquiring a set of observed signaldata values from an asset; producing a calculated set of estimatedsignal data values correlative to the set of observed signal data valuesacquired from the asset; comparing the set of observed signal datavalues to the calculated set of estimated signal data values;determining a presence of a disagreement between the set of observedsignal data values and the calculated set of estimated signal datavalues on the basis of the comparison step, and determining a cause of adetermined presence of disagreement between the set of observed signaldata values and the calculated set of estimated signal data values forperforming asset surveillance. The method further including the step ofperforming asset control as a function of the determined cause of thedetermined presence of disagreement.

[0018] In another embodiment of the invention, a method and system fordetermining asset status includes the steps of creating a faultdetection model comprised of a plurality of fault detection submodelseach having an operating mode associated thereto; creating a faultclassification model comprised of a plurality of fault classificationsubmodels each having an operating mode associated thereto; acquiring aset of observed signal data values from an asset; determining at leastone operating mode of the asset for the set of observed signal datavalues; selecting at least one fault detection submodel from the faultdetection model as a function of at least the one determined operatingmode; determining at least one fault indication as a function of theobserved signal data values; selecting at least one fault classificationsubmodel from the fault classification model as a function of at leastthe one determined operating mode, and using at least the one faultindication and at least the one selected fault classification submodelfor classifying faults for performing asset surveillance. The method andsystem of further including the step of creating a parameter estimationmodel comprised of a plurality of parameter estimation submodels eachcorrelative to at least one training data subset partitioned from anunpartitioned training data set and each having an operating modeassociated thereto and wherein the step of determining at least onefault indication as a function of the observed signal data valuesincludes the step of determining at least one fault indication as afunction of both the estimated signal values determined using theparameter estimation model and the observed signal data values.

[0019] Moreover, having thus summarized the invention, it should beapparent that numerous modifications and adaptations may be resorted towithout departing from the scope and fair meaning of the presentinvention as set forth hereinbelow by the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

[0020]FIG. 1 is a schematic functional flow diagram of an embodiment ofthe invention.

[0021]FIG. 2 is a schematic functional flow diagram of a method andsystem for training an operating mode partitioned decision model usingrecorded observations of the actual process signals in an embodiment ofthe invention.

[0022]FIG. 3 is a schematic functional flow diagram of a method andsystem for performing surveillance of an asset using an operating modepartitioned decision model in an embodiment of the invention.

[0023]FIG. 4 is a functional schematic of an operating mode partitionedtraining data set.

[0024]FIG. 5 is a functional schematic of an operating mode partitioneddecision model having an operating mode partitioned parameter estimationmodel, an operating mode partitioned fault detection model, and anoperating mode partitioned fault classification model.

[0025]FIG. 6 is a functional schematic of an operating mode partitioneddecision model having an operating mode partitioned fault detectionmodel, and an operating mode partitioned fault classification model.

[0026]FIG. 7 is a functional schematic of an operating mode partitionedfault classification model.

[0027]FIG. 8 is a schematic functional flow diagram of a preferredmethod and system for classifying faults based on observed signal valuesfor performing asset surveillance.

[0028]FIG. 9 is a schematic functional flow diagram of a surveillancesystem using an operating mode partitioned decision model in anembodiment of the invention.

[0029]FIG. 10 is a schematic functional flow diagram of a method andsystem for an unpartitioned MSET training procedure.

[0030]FIG. 11 is a schematic functional flow diagram of a method andsystem for an unpartitioned MSET surveillance procedure.

[0031]FIG. 12 illustrates the relationship between the overall MSETparameter estimation error and the number of observation vectors used inthe process memory matrix when unpartitioned parameter estimationmethods are used for MSET training and surveillance;

[0032]FIG. 13 illustrates the relationship between the data processingtime required for producing an MSET parameter estimate and the number ofobservation vectors used in the process memory matrix when unpartitionedparameter estimation methods are used for MSET training andsurveillance.

[0033]FIG. 14 is a schematic functional flow diagram of the trainingprocedure for an embodiment using an operating mode partitionedcollection of MSET parameter estimation submodels in an embodiment ofthe invention.

[0034]FIG. 15 is a schematic functional flow diagram of the surveillanceprocedure for an embodiment using an operating mode partitionedcollection of MSET parameter estimation submodels in an embodiment ofthe invention.

[0035]FIG. 16 is a schematic architecture diagram of a learning vectorquantization neural network useful for determining the operating mode ofan asset in an embodiment of the invention.

[0036]FIG. 17 lists the learning vector quantization neural networkoperating mode determiner design characteristics used for feasibilitytesting of an embodiment of the invention.

[0037]FIG. 18 lists the Space Shuttle Main Engine parameters used forfeasibility testing in an embodiment of the invention.

[0038]FIG. 19 lists the Space Shuttle Main Engine flight telemetry datasets used for feasibility testing of an embodiment of the invention.

[0039]FIG. 20 lists the parameter estimation model and submodelconfigurations used for feasibility testing of an embodiment of theinvention.

[0040]FIG. 21 lists the Space Shuttle Main Engine operating modepartitioning rules used for feasibility testing of an embodiment of theinvention.

[0041]FIG. 22 lists the feasibility test results for nominal flight datausing an embodiment of an operating mode partitioned MSET estimationmodel for the Space Shuttle Main Engine in an embodiment of theinvention.

[0042]FIG. 23 lists the feasibility test results for signal driftfailure detection simulations using an embodiment of an operating modepartitioned MSET estimation model for the Space Shuttle Main Engine inan embodiment of the invention.

[0043]FIG. 24 lists the comparative test results for nominal flight datausing an unpartitioned MSET estimation model for the Space Shuttle MainEngine containing one hundred fifty observation vectors.

[0044]FIG. 25 lists the comparative test results for signal driftfailure detection using a unpartitioned MSET estimation model for theSpace Shuttle Main Engine containing one hundred fifty observationvectors.

[0045]FIG. 26 lists the comparative test results for nominal flight datausing a unpartitioned MSET estimation model for the Space Shuttle MainEngine containing three hundred observation vectors.

[0046]FIG. 27 lists the comparative test results for signal driftfailure detection using a unpartitioned MSET estimation model for theSpace Shuttle Main Engine containing three hundred observation vectors.

[0047]FIG. 28 illustrates a mathematical parameter estimation model of atype used for Space Shuttle Main Engine telemetry data surveillance inan another embodiment.

[0048]FIG. 29 is a schematic functional flow diagram of a surveillancesystem having two X-33 Single Stage to Orbit Demonstrator vehiclehydrogen sensors.

[0049]FIG. 30 is a functional schematic of a fault classificationsubmodel for a system having two X-33 Single Stage to Orbit Demonstratorvehicle hydrogen sensors.

[0050]FIG. 31 illustrates faults and their probable causes for hydrogensensor 1 using a Bayesian Belief Network in an embodiment of theinvention.

[0051]FIG. 32 illustrates faults and their probable causes for hydrogensensor 2 using a Bayesian Belief Network in an embodiment of theinvention.

[0052]FIG. 33 lists the feasibility test results for a partitioned modelunder operating conditions in an embodiment of the invention.

[0053]FIG. 34 lists the feasibility test results for a partitioned modelunder venting conditions in an embodiment of the invention.

[0054]FIG. 35 lists the comparative test results for an unpartitionedmodel under operating conditions.

[0055]FIG. 36 lists the comparative test results for an unpartitionedmodel under venting conditions.

DETAILED DESCRIPTION OF THE INVENTION

[0056] Considering the drawings, wherein like reference numerals denotelike parts throughout the various drawing figures, reference numeral 10is directed to the system according to the instant invention.

[0057] In its essence, and referring to FIG. 1, the system 10 isgenerally comprised of a method and apparatus for performing highsensitivity surveillance of a wide variety of assets includingindustrial, utility, business, medical, transportation, financial, andbiological processes and apparatuses wherein such process and/orapparatus asset preferably has at least two distinct modes or domains ofoperation (e.g., transient and steady state modes or domains). Thesystem includes a training procedure 20 wherein a decision model 50 ofan asset 12 (e.g., a process and/or apparatus) is derived fromhistorical operating data using at least one of a plurality ofcomputer-assisted techniques. Historical operating data includes a setof observations from normal operation of the asset 12 that is acquiredand digitized by a data acquisition means 40 using any combination ofelectronic data acquisition hardware and signal processing softwareknown to those having ordinary skill in the art, and informed by thepresent disclosure. Additionally, and as delineated infra, one hallmarkof the instant invention is an operating mode partitioning method of adecision model 50 for the asset 12 that is performed during the trainingprocedure 20.

[0058] The system 10 further includes a surveillance procedure 60wherein the operating mode partitioned decision model 50 is used forhigh sensitivity computer-assisted surveillance of the asset 12 for thepurpose of determining whether a process fault or failure necessitatesan alarm or control action. Another hallmark of the instant invention,as delineated hereinbelow, is the use of the operating mode partitioneddecision model 50 as an element of the surveillance procedure 60. Thesystem 10 described herein is useful for ultra-sensitive detection ofthe onset of sensor or data signal degradation, component performancedegradation, and process operating anomalies.

[0059] Description of the Training Procedure:

[0060] More specifically, and referring to FIG. 2, the trainingprocedure 20 of the system 10 includes a method and apparatus fortraining or preparing the decision model 50 using historical operatingdata from the asset 12 that has been acquired by the data acquisitionmeans 40 using any combination of conventional electronic dataacquisition hardware and signal processing software as is well known inthe art. Upon acquiring the data, the model designer proceeds toimplement the unique method for the training procedure 20 in accordancewith instant invention. The historical operating data is acquired indigital format and stored using a data storage procedure 22. The uniquemethod for the training procedure 20 uses an operating modedetermination procedure 26 to partition the historical operating datainto one or more training data subsets 28 that together comprise atraining data set 24 wherein each training data subset 28 isrepresentative of a single operating mode i (M_(i)) wherein M_(i) is anymode between Mode 1 (M₁) to Mode N (M_(N)) where N is a positive integeror each training data subset 28 is representative of a subset ofoperating modes of the asset 12. The training data set 24 includes atleast K discrete observations of the asset 12 wherein each singleobservation, herein denoted Xobs, is comprised of a vector of datavalues for at least each signal parameter to be included in the decisionmodel 50. For the purposes of the training procedure 20, the number ofobservations, K, acquired is at least great enough to adequately boundthe operating state space of the asset 12. Thus, the training data set24 provides a representative sample of the signals produced by the asset12 during all normal modes of operation.

[0061] Again referring to FIG. 2, the unique method for the trainingprocedure 20 also includes at least one of a parameter estimationsubmodel creation procedure 29, a fault detection submodel creationprocedure 30, or a fault classification submodel creation procedure 31for creating at least one decision submodel for inclusion in thedecision model 50 using at least one training data subset 28. Inpractice, the designer first selects the operating modes that will beincluded in the decision model 50 by means of an operating mode enableprocedure 32. The method thereafter is comprised of a training loopwherein each possible operating mode of the asset 12 is assessed forinclusion in the decision model 50.

[0062] The training loop is in general controlled by two decisionprocedures. The mode enabled decision procedure 34 determines whetherthe designer intends a specific operating mode to be included in thedecision model 50. If the operating mode is not to be included, nofurther processing is required and the training loop proceeds to thenext possible operating mode as controlled by the more modes decisionprocedure 36. If the operating mode is to be included, the training datasubset 28 associated with the currently selected operating mode isselected from the training data set 24. Depending on the preference ofthe designer implementing the training loop, the operating modedetermination and training data subset extraction procedures may be, ingeneral, performed as needed or in advance of the submodel creationloop. The submodel creation loop shown in FIG. 2 illustrates operatingmode determination and training data subset extraction in advance ofimplementing the submodel creation loop but is not intended to constrainthe method to preclude determination and extraction on an as neededbasis. The result of each submodel creation loop is the addition of oneor more submodels to the decision model 50.

[0063] Still referring to FIG. 2, the operating mode determinationprocedure 26 used to classify each observation included in the trainingdata set 24 may be, in general, performed using any method suitable fordetermining the operating mode of the asset 12 given an observation orseries of observations therefrom. Methods suitable for the operatingmode determination procedure 26 include, but are not limited to, aplurality of mathematical or logic sequence techniques, a plurality ofexpert system techniques, a plurality of fuzzy logic techniques, aplurality of determined similarity techniques, a plurality of clusteringtechniques, and a plurality of neural network techniques.

[0064] Continuing to refer to FIG. 2, the parameter estimation submodelcreation procedure 29 may be, in general, performed using any methodsuitable for defining a parameter estimation model useful for estimatingthe values of one or more observed signals. Methods suitable for theparameter estimation submodel creation procedure 29 include, but are notlimited to, a plurality of multivariate state estimation techniques, aplurality of neural network techniques, a plurality of mathematicalmodel techniques, a plurality of autoregressive moving averagetechniques, a plurality of principal component analysis techniques, aplurality of independent component analysis techniques, a plurality ofdetermined similarity techniques, and a plurality of Kalman filtertechniques. Each parameter estimation submodel contained in the decisionmodel 50 may be created to implement any of a plurality of parameterestimation techniques. Further, the parameter estimation techniqueimplemented for an individual submodel is not constrained to be the sameas the parameter estimation technique implemented for any other submodelcontained in the decision model 50.

[0065] Continuing to refer to FIG. 2, the fault detection submodelcreation procedure 30 may be, in general, performed using any methodsuitable for defining a fault detection model useful for detecting faultindications on the basis of the values of one or more observed signals.Methods suitable for the fault detection submodel creation procedure 30include, but are not limited to, a plurality of sequential probabilityratio test techniques, a plurality of hypothesis test techniques, aplurality of neural network techniques, a plurality of mathematicalmodel techniques, a plurality of comparison threshold techniques, aplurality of limit comparison techniques, a plurality of determinedsimilarity techniques, and a plurality of trend analysis techniques.Each fault detection submodel contained in the decision model 50 may becreated to implement any of a plurality of fault detection techniques.Further, the fault detection technique implemented for an individualsubmodel is not constrained to be the same as the fault detectiontechnique implemented for any other submodel contained in the decisionmodel 50.

[0066] Continuing to refer to FIG. 2, the fault classification submodelcreation procedure 31 may be, in general, performed using any methodsuitable for defining a fault classification model useful fordetermining the presence, source or cause of an unacceptable assetstatus or condition on the basis of one or more fault indications.Methods suitable for the fault classification submodel creationprocedure 31 include, but are not limited to, a plurality of Bayesianbelief network techniques, a plurality of neural network techniques, aplurality of decision tree techniques, a plurality of expert systemtechniques, a plurality of rule-based techniques, a plurality ofdetermined similarity techniques, a plurality of hypothesis testtechniques, and a plurality of procedural logic techniques. Each faultclassification submodel contained in the decision model 50 may becreated to implement any of a plurality of fault classificationtechniques. Further, the fault classification technique implemented foran individual submodel is not constrained to be the same as the faultclassification technique implemented for any other submodel contained inthe decision model 50.

[0067] The unique method for the training procedure 20 is completed attraining complete point 37 when all expected operating modes of theasset 12 have been assessed. At this point, the decision model 50includes parameter estimation, fault detection, and/or faultclassification submodels for each operating mode enabled by thedesigner. The decision model 50 is thereafter useful for performingsurveillance of the asset 12.

[0068] Description of the Surveillance Procedure:

[0069] More specifically, and referring to FIG. 3, the surveillanceprocedure 60 is comprised of acquiring successive vectors of operatingdata and determining for each such observation vector whether theoperating data is indicative of an unacceptable status or condition ofthe asset 12. The surveillance procedure 60 further includesimplementing an alarm or control action for the purpose of notifying anoperator or taking a corrective action in response to a detectedunacceptable status or condition of the asset 12. The surveillanceprocedure 60 is in general an open-ended data acquisition and analysisloop that continues until such time as the operator chooses to terminatethe surveillance.

[0070] Again referring to FIG. 3, the surveillance procedure begins withan observation acquisition procedure 62 for acquiring a vector ofobserved signal data values, herein denoted Xobs. Signal data values areacquired by the data acquisition means 40 using any combination ofconventional electronic data acquisition hardware and signal processingsoftware as noted supra. Next the operating mode determination procedure26 is used to determine the operating mode for the vector of observedsignal data values, Xobs. It is essential only that the operating modedetermination procedure 26 used during the surveillance procedure 60 isthe same operating mode determination procedure 26 used during thetraining procedure 20. Upon determination of the operating modeassociated with the observed signal data, the decision submodels for thecurrent operating mode are selected from the collection of submodelscontained in the decision model 50 using a decision submodel selectionprocedure 64. The selected decision submodels for the current operatingmode may then be used with a parameter estimation procedure 66 toproduce a current vector of estimated signal data values, herein denotedXest. It is essential only that the parameter estimation procedure 66used during the surveillance procedure 60 is the same parameterestimation procedure 66 for which the decision submodel was trainedusing the parameter estimation submodel creation procedure 29 during thetraining procedure 20. The current vector of estimated signal datavalues, Xest, in general includes at least one estimated signal datavalue corresponding to at least one actual signal data value included inthe current vector of observed signal data values, Xobs. A series ofestimated signal data values produced by successive observation andparameter estimation cycles is termed herein a “virtual signal” for thesignal parameter.

[0071] Still referring to FIG. 3, the current vector of estimated signaldata values, Xest, may be in general compared to the current vector ofobserved signal data values, Xobs, using a fault detection procedure 68.The fault detection procedure 68 serves the useful purpose ofdetermining whether the current vector of observed signal data valuesindicates an unacceptable status or condition of the asset 12. The faultdetection procedure 68 may be performed using any one of a plurality ofcomparative techniques.

[0072] The results of the fault detection procedure 68 might detectfaults based on the current vector of observed signal data values. Inmany cases, fault detection quality is improved by using a faultindication decision procedure 70 that incorporates logic for consideringone or more fault detection results in making the fault indicationdecision. The fault indication decision procedure 70 may be in generalperformed using any method suitable for ascertaining a fault indicationgiven a fault detection result or series of fault detection results.Methods suitable for the fault indication decision procedure 70 include,but are not limited to, single observation techniques (e.g., alarm onevery detected fault), multi-observation voting techniques (e.g., alarmwhen X out of Y observations contain a fault indication), andconditional probability techniques (e.g., compute the fault probabilitygiven a series of fault detection results).

[0073] When faults are indicated by the fault indication decisionprocedure 70, the unique method for the surveillance procedure 60provides for a fault classification procedure 76. The faultclassification procedure is useful for determining the presence, sourceor cause of an unacceptable asset status or condition on the basis ofone or more fault indications. The classified fault is then provided tothe alarm or control action procedure 74 for the useful purpose ofenabling an automated or operator directed corrective action or warning.

[0074] Upon completing the fault indication decision procedure 70 or thealarm or control action procedure 74, the surveillance procedure thenrepeats for as long as a more data decision procedure 72 determines thatadditional surveillance data are available or terminates at surveillancecomplete step 75 when no more surveillance data are available.

[0075] Continuing to refer to FIG. 3, the usefulness of the instantinvention is, inter alia, the improvement achieved in the accuracy ofthe fault decision made by the fault classification procedure 76.Improving the accuracy of the fault classification procedure 76accomplishes a reduction in the number of false alarms sent to a processoperator or control system that can in turn result in an erroneous alarmor control action by the alarm or control action procedure 74. Further,improving the accuracy of the fault classification procedure 76accomplishes a reduction in the number of missed alarms therebyaccomplishing more timely alarm or control action by the alarm orcontrol action procedure 74. The instant invention thereby enablesimproved operating safety, improved efficiency and performance, andreduced maintenance costs for a wide variety of industrial, utility,business, medical, transportation, financial, and biological processesand apparatuses wherein such process and/or apparatus asset 12preferably has at least two distinct modes or domains of operation.

[0076]FIG. 4 shows the training data set 24 partitioned into a pluralityof training data subsets 28 wherein the operating mode associated witheach training data subset 28 is determined using the operating modedetermination procedure 26.

[0077]FIG. 5 shows an example of the decision model 50 that is comprisedof the parameter estimation model 52, the fault detection model 54, andthe fault classification model 56. FIG. 5 further shows that theparameter estimation model 52 is comprised of at least one parameterestimation submodel 53, that the fault detection model 54 is comprisedof at least one fault detection submodel 55, and that the faultclassification model 56 is comprised of at least one fault detectionsubmodel 57.

[0078]FIG. 6 shows an example of the decision model 50 that is comprisedof the fault detection model 54 and the fault classification model 56.FIG. 6 is intended to illustrate that any of the parameter estimationmodel 52, the fault detection model 54, and/or the fault classificationmodel 56 might not be used in some cases for accomplishing the assetsurveillance function.

[0079]FIG. 7 shows an example of the fault classification model 56. FIG.7 is intended to illustrate that the each of the fault detectionsubmodels 57 might be uniquely configured to more accurately accomplishthe asset surveillance function for a particular operating mode. Forexample, in FIG. 7 each of the fault classification submodels shown areuniquely configured.

[0080] Referring to FIG. 7, Submodel 1 will typically operate asfollows. If only Indication 1 is abnormal the condition will beclassified as Fault 1. If only Indication 2 is abnormal the conditionwill be classified as Fault 2. If both Indication 1 and Indication 2 areabnormal the condition will be classified as Fault 3.

[0081] In contrast and still referring to FIG. 7, Submodel i willtypically operate as follows. If only Indication 1 is abnormal thecondition will be classified as Fault 1. If only Indication 2 isabnormal the condition will be classified as Fault 2. If both Indication1 and Indication 2 are abnormal the condition will be classified as bothFault 1 and Fault 2.

[0082] In additional contrast and still referring to FIG. 7, Submodel Nwill typically operate as follows. If only Indication 1 is abnormal thecondition will be classified as Fault 1. If only Indication 2 isabnormal the condition will be classified as either Fault 3 or as nofault depending on the importance of Indication 1 for confirming thepresence of Fault 3. If both Indication 1 and Indication 2 are abnormalthe condition will be classified as Fault 3.

[0083] Thus, still referring to FIG. 7, the behavior of the faultclassification procedure 76 using the fault classification model 56 canbe tailored to suit the decision model 50 designer's requirements forthe asset surveillance function.

[0084]FIG. 8 shows the steps used to determine the status or conditionof the asset 12 using the classification of faults during assetsurveillance. The first step is to acquire observed signal values fromthe asset 12 using the observation acquisition procedure 62. The secondstep is to determine corresponding estimated signal values using theparameter estimation procedure 66. The third step is to determine thepresence of any fault indications using the fault detection procedure68. The fourth step is to classify the presence, source and/or cause ofthe fault indications, if any, using the fault classification procedure76. These steps repeat until terminated by the more data decisionprocedure 72.

[0085]FIG. 9 outlines a general surveillance procedure of the system 10when employing the operating mode partitioned decision model 50. In atypical surveillance procedure, the asset 12 is the source of at leastone signal source 42 that is acquired and digitized using conventionaldata acquisition means 40 for providing the data acquisition procedurefor the purpose of computer-assisted surveillance. The digitized signaldata is generally evaluated using a computer 44 having computer softwaremodules implementing the various procedures describe supra, such as theoperating mode determination procedure 26, and further providing thememory means for the decision model 50. The operating mode determinationprocedure 26 is used to determine the current operating mode of theasset 12 given the acquired process signal data. The decision model 50provides the operating mode partitioned parameter estimation model 52that in turn provides the parameter estimation submodel 53 that is usedto produce an estimated signal value for at least one signal source 42emanating from the asset 12. The parameter estimation submodel 53 ingeneral uses the parameter estimator procedure 66 to produce theestimated signal values. The parameter estimation submodel 53 selectedfrom the decision model 50 and used by the parameter estimationprocedure 66 is dependent on the operating mode determined by theoperating mode determination procedure 26.

[0086] The observed signal values and/or the estimated signal values arethen generally evaluated to identify the presence of any unacceptablestatus or condition of the asset 12. The decision model 50 provides theoperating mode partitioned fault detection model 54 that in turnprovides the fault detection submodel 55 that is used to detect theindications of any unacceptable status or condition of the asset 12. Thefault detection submodel 55 in general uses the fault detectionprocedure 68 to detect the indications of faults. The fault detectionsubmodel 55 selected from the decision model 50 and used by the faultdetection procedure 68 is dependent on the operating mode determined bythe operating mode determination procedure 26.

[0087] The fault indications, if any, are then generally evaluated toclassify the presence, source and/or cause of any unacceptable status orcondition of the asset 12. The decision model 50 provides the operatingmode partitioned fault classification model 56 that in turn provides thefault classification submodel 57 that is used to classify anyunacceptable status or condition of the asset 12. The faultclassification submodel 57 in general uses the fault classificationprocedure 76 to classify the indications of faults. The specific faultclassification submodel 57 selected from the decision model 50 and usedby the fault classification procedure 7668 is dependent on the operatingmode determined by the operating mode determination procedure 26.

[0088] The results of the fault classification are thereaftercommunicated by a conventional communications link 80 (as is known tothose having ordinary skill in the art, and informed by the presentdisclosure) to an operator console 82 or automated process controlsystem 84 for possible alarm and/or control action.

[0089] The computer 44 along with its typically associated memory meanscan also be employed to perform the training and surveillance procedures20, 60 as delineated supra and to store all the data associated withthese procedures, for example, the historical operating data, thetraining data and decision model.

[0090] MSET Procedure:

[0091] In an embodiment of the invention, the method used for parameterestimation is a multivariate state estimation technique (MSET)procedure. The US Department of Energy's Argonne National Laboratorydeveloped the implementation of MSET described herein for surveillanceof sensors and components in nuclear power plant applications. However,other implementations of a multivariable state modeling technique, forexample multivariable linear regression, are possible and useful inconjunction with the instant invention. MSET is in general astatistically derived parameter estimation algorithm that uses advancedpattern recognition techniques to measure the similarity or overlapbetween signals within a defined operational domain wherein the domainis defined by a set of operating examples. MSET “learns” patterns amongthe signals by numerical analysis of historical process operating data.These learned patterns or relationships among the signals are then usedto estimate the expected signal values that most closely correspondswith a new signal data observation. By quantifying the relationshipbetween the current and learned states, MSET estimates the currentexpected response of the process signals. MSET parameter estimates arethen used with a form of statistical hypothesis testing, such as thesequential probability ratio test (SPRT) or similar probability ratiotest algorithm (as shown in U.S. Pat. No. 5,459,675 and which is herebyincorporated by reference in its entirety) to compare the currentestimated value of a signal with its observed value. The statisticalhypothesis comparison test provides a sensitive and widely applicablemethod to detect a fault or failure in an asset. However, otherimplementations of the comparison test are possible and useful inconjunction with the instant invention.

[0092] An MSET parameter estimation model is created for the asset 12using the MSET training algorithms to learn the inherent datarelationships within a set of historical process operating data. A SPRTfault detection model is calibrated using the MSET parameter estimationmodel and the set of historical process operating data. The trained MSETmodel is then used with the MSET parameter estimation procedure and theSPRT fault detection procedure to perform the process surveillancefunction when presented with a new observation of signal data values.The following sections will first provide a mathematical overview of theMSET algorithms and procedures useful for training a parameterestimation model and for using this trained model for processsurveillance. The description is followed by a detailed description of apreferred embodiment of the instant invention using a novel operatingmode partitioned parameter estimation model for asset surveillance.

[0093] Description of the MSET Training and Surveillance Procedures:

[0094] The MSET methods are generally described in the following two USGovernment documents produced and maintained by the US Department ofEnergy's Argonne National Laboratory, Argonne, Ill., disclosure of whichis incorporated in its entirety herein by reference. The MSET methodswere embodied in MSET software provided by Argonne National Laboratoryunder NASA Contracts NAS4-99012 and NAS13-01001 and were not modified orin themselves improved for the purposes of a-preferred embodiment.

[0095] J. P. Herzog, S. W. Wegerich, R. M. Singer, and K. C. Gross,“Theoretical Basis of the Multivariate State Estimation Technique(MSET),” Argonne National Laboratory, ANL-NT-49, December 1997.

[0096] J. P. Herzog, S. W. Wegerich, K. C. Gross, and R. M. Singer,“MSET: Code Structure and Interface Development Guide,” ANL-NT-48,August 1997.

[0097] The MSET algorithm uses pattern recognition with historicaloperating data from an asset to generate one form of a parameterestimation model. If data are collected from a process over a range ofoperating points, these data can be arranged in matrix form, where eachcolumn vector (a total of m) in the matrix represents the measurementsmade at a particular point. Thus, this matrix will have the number ofcolumns equal to the number of operating points at which observationswere made and the number of rows equal to the number of measurements (atotal of n signal data values) that were available at each observation.We begin by defining the set of measurements taken at a given time t_(j)as an observation vector X(t_(j)),

{right arrow over (X)}(t _(j))=[x ₁(t _(j)),x ₂(t _(j)), . . . , x_(n)(t _(j)) ]^(T)   (1)

[0098] where x_(i)(t_(j)) is the measurement from signal i at timet_(j). We then define the data collection matrix as the process memorymatrix D: $\begin{matrix}{\overset{\leftrightarrow}{D} = {\begin{bmatrix}d_{1,1} & d_{1,2} & \cdots & d_{1,m} \\d_{2,1} & d_{2,2} & \cdots & d_{2,m} \\\quad & {\vdots \quad} & \quad & \quad \\d_{n,1} & d_{n,2} & {\quad \cdots \quad} & d_{n,m}\end{bmatrix} \equiv \left\lbrack {{\overset{\rightarrow}{X}\left( t_{1} \right)},{\overset{\rightarrow}{X}\left( t_{2} \right)},\quad \ldots \quad,{\overset{\rightarrow}{X}\left( t_{m} \right)}} \right\rbrack}} & (2)\end{matrix}$

[0099] Each of the column vectors (X(t_(j))) in the process memorymatrix represents an operating point of the process. Any number ofobservation vectors can be assigned to the process memory matrix.Training an MSET model includes collecting enough unique observationvectors from historical operation of the process during normalconditions such that the process memory matrix encompasses the fulldynamic operating range of the process. Computation of the D matrix isthe first of three steps in the method for training an MSET model basedon historical operating data.

[0100] One of at least two algorithms is used by MSET to select thevectors in the D matrix. The MinMax algorithm extracts vectors thatbound the vector space defined by the training data and returns thesmallest process memory matrix that will produce an effective systemmodel (see also U.S. Pat. No. 5,764,509 and which is hereby incorporatedby reference in its entirety). The Vector Ordering algorithm selects andincludes representative vectors from the inner regions of the vectorspace producing a more accurate system model.

[0101] Once the process memory matrix has been constructed, MSET is usedto model the dynamic behavior of the system. For each currentobservation of the system (Xobs), MSET compares the observation vectorto the stored operating points to calculate an estimate of the processparameter values. The parameter estimate of the current process state(Xest) is an n-element vector that is given by the product of theprocess memory matrix and a weight vector, W:

{right arrow over (X)} _(est)

=

{right arrow over (W)}  (3)

[0102] The weight vector represents a measure of similarity between theestimate of the current operating point and the elements of the processmemory matrix. To obtain the weight vector, we minimize the errorvector, R, where:

{right arrow over (R)}={right arrow over (X)} _(obs) −{right arrow over(X)} _(est)   (4)

[0103] The error is minimized for a given operating point when:

{right arrow over (W)}=(

^(T){circle over (×)}

)⁻¹(

^(T) {circle over (×)}{right arrow over (X)} _(obs))   (5)

[0104] This equation represents a “least squares” minimization when thepattern recognition operator {circle over (×)} is the matrix dotproduct. The Argonne MSET software includes a choice of several patternrecognition operators that provide excellent parameter estimationperformance (for example, see U.S. Pat. No. 5,764,509 and U.S. Pat. No.5,987,399 each hereby incorporated by reference in their entirety).

[0105] Once the weight vector is found, the resulting current estimateof the signal data values (i.e., the parameter estimate vector) is givenby:

{right arrow over (X)} _(est)=

(

^(T){circle over (×)}

)⁻¹(

^(T) {circle over (×)}{right arrow over (X)} _(obs))   (6)

[0106] The first application of the pattern recognition operator inequation (6) (D^(T){circle over (×)}D) involves a comparison between therow vectors in the D^(T) matrix and each of the column vectors in the Dmatrix. If we define G=D^(T){circle over (×)}D, then G, the similaritymatrix, is an m by m matrix. The element in the i-th row and j-th columnof the matrix (g_(i,j)) represents a measure of the similarity betweenthe i-th and j-th column vectors (i.e., memorized operating points) inthe process memory matrix. The second application of the patternrecognition operator in equation (6) (D^(T){circle over (×)}X_(obs))involves a comparison between the row vectors in the D^(T) matrix andeach of the elements in the observation vector Xobs. If we defineA=D^(T){circle over (×)}X_(obs), then A, the similarity vector, is an mby 1 vector. Each element in the similarity vector is a measure of thesimilarity between the observation vector and the i-th column vector(i.e., memorized operating points) in the process memory matrix.

[0107] Note that the similarity matrix is a function of the processmemory matrix only. Thus, the similarity matrix and its inverseGinv=(D^(T){circle over (×)}D)⁻¹ can be calculated as soon as theprocess memory matrix has been derived thereby making the application ofMSET to an on-line surveillance system more computationally efficient.Computation of the Ginv matrix initializes the parameter estimationmodel and completes the second of three steps in the procedure fortraining an MSET model based on historical operating data.

[0108] The third and final step in the training procedure includesanalyzing the historical training data using equation (6) tocharacterize the expected statistical mean and variance of the residualerror vector, R, for each signal parameter in the observation vector.The resulting mean vector, M, is later used in the surveillanceprocedure to normalize the residual error for each observation evaluatedusing the statistical hypothesis test fault detection procedure. Theresulting variance vector, V, is later used at the beginning of thesurveillance procedure to initialize the fault detection thresholdvalues used in the statistical hypothesis test fault detectionprocedure.

[0109]FIG. 10 illustrates the procedure for training an MSET parameterestimation model. The procedure is used to produce an unpartitioned MSETmodel 102 that is not partitioned by operating mode. The MSET trainingprocedure developed by Argonne National Laboratory (ANL) as describedherein is embodied in one instance within the ANL software modules knownas train.c and sys_mod.c. As described herein above, the MSET trainingprocedure begins with a MSET model extraction procedure 90 used topopulate a process memory matrix 92 (D) from the training data set 24(historical process operating data). The MSET model extraction procedure90 makes use of at least one of a plurality of observation vectorextraction methods embodied in one instance within the ANL train.csoftware module, including but not limited to the MinMax method, and theVector Ordering method. A MSET model initiation procedure 94 is thesecond step of the method and is used to initialize the MSET decisionmodel by the computation of a inverse similarity matrix 96 (Ginv). TheMSET model initiation procedure 94 makes use of at least one of aplurality of pattern recognition operator methods embodied in oneinstance within the ANL sys_mod.c software module, including but notlimited to the SSA method, the BART method, the VPR method, the VSETmethod, and the PSEM method. The third step of the MSET trainingprocedure uses the process memory matrix 92 and the inverse similaritymatrix 96 to perform a MSET training data analysis procedure 98 usingthe training data set 24. The training data analysis procedure 98 usesthe MSET model to computes the residual error mean and variance vectors100 (M and V, respectively) over the training data. The trainingprocedure is in general performed once for the training data set 24 thuspreparing an unpartitioned MSET model 102 for use in the MSETsurveillance procedure.

[0110] In the MSET surveillance procedure, new operating dataobservations are evaluated sequentially using the unpartitioned MSETmodel 102 for the purposes of validating the data or discerning ananomalous (not normal) process operating condition. For each newobservation vector, Xobs, presented to the MSET parameter estimationmethod, the expected operating state having the greatest similarity tothe current observed state is returned as a parameter estimate vector,Xest. Diagnostic decisions are then made on the basis of the difference(residual error) between the observed and estimated values for at leastone process signal parameter contained in the estimate vector. Faultindications are determined using at least one of a plurality of faultdetection methods including, but not limited to, a threshold limit testmethod, a Sequential Probability Ratio Test (SPRT) method, and aBayesian Sequential Probability (BSP) test method to produce a faultindication based on the value of the residual error for at least oneprocess parameter.

[0111]FIG. 11 illustrates the method and system for MSET-basedsurveillance. The MSET surveillance methods as described herein areembodied in one instance within the ANL software modules known assys_mod.c and fault_detect.c. Prior to performing surveillance for newoperating data observations, a MSET fault detector initializationprocedure 106 is performed. The MSET fault detector initializationprocedure 106 takes the variance (V) vector 100 and several otherconstants as its arguments. The initialization procedure makes use ofone of a plurality of fault detection methods embodied in one instancewithin the ANL fault_detect.c software module, including but not limitedto the SPRT method, and the BSP method. The MSET surveillance procedurethen proceeds by sequentially acquiring and evaluating each new dataobservation until such time as surveillance is completed. Dataobservations are acquired using the observation acquisition procedure62. For each new observation vector, Xobs, a parameter estimate vector,Xest, is produced by the parameter estimation procedure 66 using theunpartitioned MSET model 102 with the same pattern recognition operatorthat was used in the MSET training procedure. The residual error vector,R, is computed and is then normalized using a residual valuenormalization procedure 108 that includes subtracting the mean (M)vector 100 from the value of the residual error. The normalized residualvector is then evaluated using the same fault detection procedure 68that was initialized at the start of the MSET surveillance procedure. Ifthe fault detection procedure 68 results in a fault determination by thefault indication decision procedure 70, the alarm or control actionprocedure 74 communicates the fault information by the conventionalcommunications link 80 (not shown) to the operator console 82 (notshown) and/or automated process control system 84 (not shown) forcorrective action. In the fault indication decision procedure 70, aBayesian conditional probability test is in general used to reach afault decision based on a series of fault detection results from thefault detection procedure 68. The surveillance procedure then repeatsfor as long as the more data decision procedure 72 determines thatadditional surveillance data is available.

[0112] Limitations of the MSET Training and Surveillance Method andSystem:

[0113] In the method and system described above, MSET is trained by theconstruction of a process memory matrix, D, based on historicaloperating data containing a collection of normal operating points of theprocess. MSET creates the process memory matrix by selectingrepresentative process data observations (herein termed observationvectors) that characterize the dynamic patterns inherent across alloperating points of the process. However, if the process can operate intwo or more distinct modes of operation, then the totality of operatingpoints for all possible operating modes must be represented in theprocess memory matrix to produce an effective MSET model. As the numberof distinct operating modes of process operation represented in thetraining data increases, one of two limitations occur:

[0114] Limitation 1. If the total number of observation vectors in theprocess memory matrix is fixed, then the number of data patterns used torepresent any single operating mode of a process decreases. Thisdirectly reduces the accuracy of MSET's parameter estimates, which mayresult in false alarms or reduce the ability of the fault detectionprocedure to reliably detect subtle sensor failures or other processanomalies.

[0115] The parameter estimation accuracy of the MSET algorithm is ingeneral an inverse power law function of the number of vectors in theprocess memory matrix. Limitation 1 is evident in the example of FIG. 12that illustrates the overall parameter estimation error versus thenumber of vectors in the process memory matrix for an unpartitioned MSETmodel of six Space Shuttle Main Engine sensors.

[0116] Limitation 2. Allowing the number of observation vectors in theprocess memory matrix to increase ameliorates Limitation 1 above, butincurs a computational performance cost. The number of computeroperations required for MSET to produce a parameter estimate scales withthe square of the number of observation vectors stored in the processmemory matrix. This is because the MSET parameter estimation algorithmmust perform pattern matching between the current operating data vectorand each element of the process memory matrix. Pattern matching uses theGinv matrix, the size of which increases as the square of the number ofobservation vectors. Processing time for MSET parameter estimation hasbeen empirically shown to follow a square law equation of the form:

Observation processing time (msec)=A+B*[Number of observation vectors inD]²   (7)

[0117] Limitation 2 is evident in the example of FIG. 13 thatillustrates the overall MSET parameter estimation processing time on a300-MHz Pentium II desktop computer versus the number of vectors in theD matrix for an unpartitioned MSET model of six Space Shuttle MainEngine sensors.

[0118] Novel Improvements to the MSET Training and SurveillanceProcedures:

[0119] Having described the MSET training and surveillance methodsherein above, this section describes the novel improvements made by theinstant invention when used for MSET training and surveillance, theimprovements being applicable to any asset preferably having at leasttwo distinct modes of operation. It is explained herein above that it isbeneficial to minimize the number of vectors in the process memorymatrix in order to optimize the processing speed of the MSET algorithm.It is further explained herein above that the MSET methods require atrade-off to be made between processing time and parameter estimationaccuracy. In the worst case, this trade-off results in unacceptableperformance for a process surveillance application. The novel solutionto this problem made by the instant invention is to use multiplecoordinated MSET parameter estimation submodels, with each submodeltrained over a limited operating mode state space. With the instantinvention, each submodel may be defined to contain only the minimumnumber of observation vectors required to adequately characterize asingle specific operating mode or related subset of modes. Since onlyone submodel must be evaluated for each data observation presented toMSET during the surveillance procedure, both parameter estimationaccuracy and processing speed are greatly improved.

[0120] The following example illustrates an unobvious benefit of theinstant invention. Consider a process that requires on-line surveillanceacross multiple modes of operation. Further consider that the safety orother critical nature of the surveillance requires fault decisionperformance within a time interval that allows for on-line MSETprocessing with a process memory matrix containing at most 100 vectors.However, further suppose that the desired fault detection accuracyrequires on-line MSET parameter estimation with a process memory matrixcontaining 300 vectors to adequately characterize the operating modestate space. In the prior art, both criteria could not be simultaneouslysatisfied. The instant invention solves this problem for many types ofprocesses and apparatuses by enabling the decision model designer topartition the operating mode state space and thus produce three 100vector submodels providing the desired level of fault detectionsensitivity (300 vectors) while having a processing speed comparable tothe 100 vector model. This implementation requires only the addition ofan operating mode determination procedure that selects the appropriatesubmodel for each new observation presented to the system and does notrequire a change to the MSET method itself.

[0121] Improved Training Procedure:

[0122] The combination of an MSET parameter estimation model 52 with anassociated SPRT fault detection model 54 is herein termed an MSETdecision model 50. An MSET decision model is one of a plurality ofpossible implementation specific instances of the decision model 50.

[0123]FIG. 14 illustrates the training procedure 20 useful for producingone form of decision model 50 or specifically a MSET decision model 50in accordance with the instant invention. The training procedureincludes and modifies the MSET training methods described in FIG. 10 andillustrated in FIG. 14 as MSET training procedure 118. With the instantinvention, the model designer may now individually specify thoseoperating modes for which MSET and SPRT training and surveillance isenabled. The training procedure loops through each defined operatingmode with the loop controlled by the mode enabled decision procedure 34and the more modes decision procedure 36. If the operating mode isenabled, a MSET decision submodel 114 is created (this is a specificexample of parameter estimation submodel creation procedure 29 and faultdetection submodel creation procedure 30 when employing MSET and SPRT)for the operating mode. In order to create the MSET decision submodel114, the operating mode specific training data subset 28 is firstextracted from the training data set 24 using the operating modedetermination procedure 26. This operating mode specific training datasubset 28 is then used to create the MSET decision submodel 114 usingthe same procedures used in the MSET training procedure 118 to create anunpartitioned MSET model. Specifically, the MSET procedures used insequence are the MSET model extraction procedure 90 to produce theprocess memory matrix 92, the MSET model initialization procedure 94 toproduce the inverse similarity matrix 96, and the MSET training dataanalysis procedure 98 to produce the residual mean and variance vectors100 used by the SPRT fault detection procedure. Note that this series ofprocedures is grouped in the general case as the parameter estimationsubmodel creation procedure 29 and fault detection submodel creationprocedure 30. The process is repeated with each loop including a MSETdecision submodel storage procedure 116 to add the MSET decisionsubmodel 114 to the MSET decision model 50 for each enabled operatingmode. At the conclusion of the training procedure 20, the operating modepartitioned MSET decision model 50 includes an array of individual MSETdecision submodels 114, one for each enabled operating mode.

[0124] The MSET decision model 50 is a collection of the operating modespecific MSET decision submodels. The MSET decision model 50 includesthe following at a minimum:

[0125] A set of process memory matrices 92 (D), one for each enabledoperating mode;

[0126] A set of inverse similarity matrices 96 (Ginv), one for eachenabled operating mode; and

[0127] A set of residual mean and variance vectors 100 (M and V), onefor each enabled operating mode.

[0128] Working together these decision submodels provide parameterestimation and fault detection over the entire operating mode statespace that the designer has selected for surveillance. An additionalnovel feature of the instant invention is that each of the decisionsubmodels in the MSET decision model 50 may be of unique dimensions thatis, each submodel may contain unique numbers of modeled signalparameters and process memory matrix vectors. A decision submodel'sdimensions may be different than the dimensions selected for any otheroperating mode thereby permitting the unobvious benefit of furtheroptimizing the MSET method and system for the surveillance requirementsof each individual operating mode of the asset. This is importantbecause certain modes of process operation are often more performance orsafety critical than others.

[0129] An additional novel feature of the instant invention is that ingeneral each of the submodels in the decision model 50 may also bespecified with unique parameter estimation and fault detector settingsfor each operating mode. This provides the unobvious benefit ofoptimizing surveillance sensitivity and performance by operating mode.Examples of optimization by operating mode include, but are not limitedto, the following:

[0130] Selection of the parameter estimation and training algorithm byoperating mode;

[0131] Selection of the fault detection algorithm by operating mode;

[0132] Selection of the fault detection procedure false alarmprobability, missed alarm probability, system disturbance magnitudevalues, or other threshold constants by operating mode;

[0133] Selection of the fault classification procedure algorithm andassociated thresholds and constants by operating mode.

[0134] Novel Improvements to the Parameter Estimation Procedure:

[0135]FIG. 15 illustrates a novel method and system for the surveillanceprocedure 60 using the MSET decision model 50 as delineated hereinabove.The surveillance procedure 60 includes and modifies the surveillancemethods described in FIG. 11 and illustrated in FIG. 15 as MSETsurveillance procedure 122, 126. With the instant invention, the modeldesigner may now individually specify those operating modes for whichMSET surveillance is enabled.

[0136] At the beginning of the surveillance procedure, the faultdetection procedures are initialized for each enabled decision submodel.Initialization of each MSET decision submodel 114 uses the same faultdetector initialization procedure 106 used for initialization of theunpartitioned model 102. The surveillance procedure thereafter includesan open-ended loop for data acquisition and surveillance processing thatis terminated by the more data decision procedure 72.

[0137] During surveillance, each new vector of observed signal datavalues, Xobs, is acquired using the data acquisition procedure 40 andthe observation acquisition procedure 62. Next, the operating modedetermination procedure 26 is used to determine the operating mode foreach new data observation, Xobs, acquired from the asset 12. If the newdata observation is determined by the mode enabled decision procedure 34to represent an operating mode that is not enabled for surveillance, nofurther processing is required until the next data observation isacquired from the asset 12. Conversely, if the new data observation isdetermined to represent an enabled operating mode, the correct MSETdecision submodel 114 is selected from the MSET decision model 50 usingthe decision submodel selection procedure 64 and all required decisionsubmodel data is loaded into the computer memory. From this point,surveillance processing occurs using the same procedures previouslydescribed for the MSET method until a fault indication is encountered.The method of the instant invention uniquely includes the faultclassification procedure 76 that was not previously used in conjunctionwith MSET. Once surveillance processing is completed, the procedurereturns to acquire a new data observation from the asset 12.

[0138] An unobvious benefit of only performing processing for selectedoperating modes is that the MSET decision model does not need to provideparameter estimation capabilities for those operating modes that do notrequire on-line surveillance. For example, it may be desirable toexclude certain modes of operation (or non-operation) from the MSETdecision model even though such modes are included within the trainingdata. The ability to explicitly exclude operating modes that do notrequire surveillance simplifies the training data acquisition proceduresand minimizes the on-line processing time required for a parameterestimation based surveillance method.

[0139] While an MSET procedure is described herein above, any type ofparameter estimation procedure can be used with the instant invention.The novelty described infra is not a modification or improvement to theMSET procedure, but is rather a new means of using any parameterestimation procedure so as to more effectively accomplish the assetsurveillance objective.

[0140] SPRT Fault Detection Procedure:

[0141] Parameter estimation techniques, such as delineated hereinabove,can provide an extremely accurate estimate of asset signals. Thedifference between the current estimates and the current signalobservations provides a set of residual values used as the indicator forsensor and equipment faults. Instead of using standard threshold limitsto detect fault conditions (i.e., declaring a fault indication when asignal's residual value exceeds a preset threshold), we havedemonstrated excellent fault detection performance using a sequentialprobability ratio test (SPRT) fault detection procedure 68 inconjunction with the present invention.

[0142] The SPRT algorithm is one instance of a family of likelihoodratio tests that improve the threshold detection process by providingmore definitive information about validity using statistical hypothesistesting. The SPRT technique is based on user-specified false alarm andmissed alarm probabilities, allowing control over the likelihood ofmissed detection or false alarms. The SPRT technique provides a superiorsurveillance tool because it is sensitive not only to disturbances inthe signal mean, but also to very subtle changes in the statisticalquality (variance, skewness, bias) of the signals. For sudden, grossfailures of a sensor or component, the SPRT will indicate thedisturbance as fast as a conventional threshold limit check. However,for slow degradation, the SPRT can detect the incipience or onset of thedisturbance long before it would be apparent with conventional thresholdlimit checks.

[0143] The SPRT fault detection procedure monitors successiveobservations of a process by analyzing the stochastic components of thesignal's residuals given by equation (4), above. Let R_(n) represent theresidual signal at a given moment t_(n) in time. Then the sequence ofvalues {R_(n)}={R₀, R₁, . . . R_(n)} should be normally distributed withmean 0, when the asset is operating normally.

[0144] The first test utilized by the SPRT is a test for a change in thesequence mean, which declares that the signal is degraded if thesequence {R_(n)} exhibits a non-zero mean, e.g., a mean of either ±Mwhere M is the user-assigned system disturbance magnitude for the meantest. The SPRT will decide between two hypotheses: H₁, where theresidual signal forms a Gaussian probability density function with meanM and variance σ²; or H₀, where the residual signal forms a Gaussianprobability density function with mean 0 and variance σ². If the SPRTaccepts H₁, we declare that the residual signal is degraded (a fault isdetected).

[0145] The SPRT fault detection procedure operates as follows. At eachtime step, a test index is calculated and compared to two thresholdlimits A and B. The test index is related to the likelihood ratio(L_(n)), which is the ratio of the probability that hypothesis H₁ istrue to the probability that hypothesis H₀ is true: $\begin{matrix}{L_{n} = \frac{{Probability}\quad {of}\quad {sequence}\left\{ R_{n} \right\} {given}\quad H_{1}\quad {true}}{{Probability}\quad {of}\quad {sequence}\left\{ R_{n} \right\} {given}\quad H_{0}\quad {true}}} & (8)\end{matrix}$

[0146] If the likelihood ratio is greater than or equal to the upperthreshold limit (i.e., L_(n)≧B), then it can be concluded thathypothesis H₁ is true. If the likelihood ratio is less than or equal tothe lower threshold limit (i.e., L_(n)≦A), then it can be concluded thathypothesis H₀ is true. If the likelihood ratio falls between the twolimits (i.e., A<L_(n)<B), then neither hypothesis can be concluded to betrue and sampling continues.

[0147] The SPRT technique allows the user to specify the targetedlikelihood of missed detection or false alarm. The following expressionsrelate the threshold limits to the misidentification probabilities:$\begin{matrix}{A = {{\frac{\beta}{1 - \alpha}\quad {and}\quad B} = \frac{1 - \beta}{\alpha}}} & (9)\end{matrix}$

[0148] where α is the probability of accepting H₁ when H₀ is true (i.e.,the false alarm probability) and β is the probability of accepting H₀when H₁ is true (i.e., the missed detection probability).

[0149] Assuming that the residual signal {R_(n)} is normallydistributed, the likelihood ratio L_(n) is given by: $\begin{matrix}{L_{n} = {\exp \left\lbrack {\frac{- 1}{2\quad \sigma^{2}}{\sum\limits_{k = 1}^{n}\quad {M\left( {M - {2r_{k}}} \right)}}} \right\rbrack}} & (10)\end{matrix}$

[0150] Defining the SPRT index for the mean test to be the exponent inequation (10), $\begin{matrix}{{SPRT}_{mean} = {{\frac{- 1}{2\quad \sigma^{2}}{\sum\limits_{k = 1}^{n}\quad {M\left( {M - {2r_{k}}} \right)}}} = {\frac{M}{\quad \sigma^{2}}{\sum\limits_{k = 1}^{n}\left( {r_{k} - \frac{M}{2}} \right)}}}} & (11)\end{matrix}$

[0151] Then by taking the natural logarithms of equations (9) and (10)the sequential sampling and decision strategy can be conciselyrepresented as:

[0152] If SPRT_(mean)≦In(β/(1−α)), then accept hypothesis H₀ as true,

[0153] If In(β/(1−α))≦SPRT_(mean)≦In((1−β)/α), then continue sampling,and

[0154] If SPRT_(mean)≧In((1−β)/α), then accept hypothesis H₁ as true.

[0155] Two indices are calculated for each of the sequential hypothesistests. For the mean test, an index is calculated for both positive andnegative values of the system disturbance magnitude, M. The systemdisturbance magnitude for the mean test specifies the number of standarddeviations the residual signal must shift in the positive or negativedirection to trigger an alarm.

[0156] The implementation of SPRT is originally described in Wald andWolfowitz, “Optimum Character of the Sequential Probability Ratio Test,”Ann. Math. Stat., 19, 326 (1948), disclosure of which is incorporatedherein by reference.

[0157] Limitations of Prior Fault Detection Procedures

[0158] Threshold limit tests, SPRTs and related techniques for faultdetection are of great practical use in the field of asset surveillanceand equipment condition monitoring. Many practitioners report theirapplications and value in a wide range of deployments. The majority ofsuch deployments use threshold limit tests; however, more recently SPRTsand related techniques have come into significant use.

[0159] A limitation of all prior fault detection techniques has been theinevitable trade off between false alarm rate and missed detection rate.When set too sensitively, the fault detector alarms as desired whenactual fault conditions occur but might also alarm when normal processvariations exceed the detector's alarm limits (false alarms). When settoo conservatively, the fault detector does not false alarm as readilybut the detection of an actual fault condition is most often delayed ormissed.

[0160] The trade off between false alarm rate and missed detection rateis aggravated when the asset is monitored over a range of operatingmodes. In practice, transient modes and certain operating modes willcommonly imbue greater levels of uncertainty in the observed assetsignals due to noise, bias, lead/lag, and other effects than will otheroperating modes. The practical result is increased uncertainty in boththe current observed signal and in the parameter estimation modeldeveloped from training data signals. This leads to larger residuals insome asset operating modes than in others. Using a single set of alarmlimits for fault detection results in higher false alarm rates for noisyoperating modes and higher missed alarm rates for less noisy operatingmodes.

[0161] Novel Improvements to the Fault Detection Procedure

[0162] Since fault detection tests commonly operate on a mathematicalfunction of the residuals or other evaluation of the observed signal,the alarm limits used for the fault detection tests are more accurateand consistent when a separate set of alarm limits are provided for eachoperating mode of the asset. The method and system of the instantinvention uniquely provides this capability.

[0163] While a SPRT procedure is described herein above, any type offault detection procedure can be used with the instant invention. Thenovelty described infra is not a modification or improvement to the SPRTprocedure, but is rather a new means of using any existing faultdetection procedure so as to more effectively accomplish the assetsurveillance objective.

[0164] BBN Fault Classification Procedure

[0165] Bayesian belief networks (BBNs) are applied in Decision Theoryunder various names, including Causal Probabilistic Networks, CausalNets, and Probabilistic Influence Diagrams. BBN methods provide accuratecalculations of expected false alarm and missed alarm rates, areresilient to missing information (e.g., instrumentation failures), andgracefully handle multiple failures. BBNs are based on the calculus ofprobabilities and provide the mathematical formalism whereby engineeringjudgment may be expressed as the degree of belief in an outcome given aset of observations. They have been used in a wide variety ofapplications to represent probabilistic knowledge for automatedreasoning. Using Bayesian probability theory, it is possible to captureengineering knowledge about the dependencies between variables and topropagate consistently and quantitatively the impact of evidence fordiagnosing the current condition of an asset. Specifically, beliefnetworks are graphical representations of a joint probabilitydistribution in which the graph nodes represent discrete-valued randomvariables and the arcs between nodes represent influences betweenvariables.

[0166] The BBN is one example of a fault classification procedure 76that might be used in conjunction with the instant invention. Otherexamples include, but are not limited to, procedural logic, rule-basedexpert systems, blackboard expert systems, decision trees, determinedsimilarity methods, various forms of probabilistic networks and neuralnetworks. We use a belief network to capture our knowledge about anasset and its operation. A key characteristic of a BBN is its ability todeal with the inherent uncertainty in our knowledge about a monitoredasset. The process of building an application-specific belief network isas follows:

[0167] Define the nodes—define the model variables to be observed orpredicted;

[0168] Define the network topology—define the user's knowledge of theconditional dependence and independence between nodes;

[0169] Define the network's probability tables—quantify statisticalknowledge, uncertainty, and the user's engineering judgments.

[0170] Once this knowledge is expressed, the belief network can performfault classification by calculating the probability states of thepredicted nodes from the tables of conditional and marginalprobabilities and the states of the observed nodes.

[0171] Bayesian belief networks allow us to express our beliefs with adegree of uncertainty. In most cases, cause and effect relationships arenot absolute, instead we hold a certain degree of belief in theserelationships. For example, if we say “Birds fly.” we accept that forthe most part birds do, in fact, fly. However, ostriches don't fly,neither do penguins, birds with broken wings, or dead or injured birds.These exceptions prevent us from saying with 100% certainty that “Birdsfly.” It is much more accurate and more natural to say that we believethat “Birds fly most of the time.” It is this ability to quantify thedegree of belief in a proposition, which makes the BBN useful for afault classification procedure.

[0172] Belief networks have four necessary characteristics:

[0173] 1. A belief network is built from nodes that represent variablesthat can take on multiple values or states. These nodes may representthe state of the sensors or fault detectors in the model, the cause ofany fault, or a decision to be made based on the evidence for a fault.

[0174] 2. Each of these nodes is connected to at least one other node bya directed link. The intuitive meaning of these links is that the parenthas a direct influence on the child. For example, there is a greaterprobability that a bird will fly if it is not injured. Theserelationships are probabilistic as opposed to being deterministic andare the reason for a link between the “Birds fly” node and the “Injured”node. A lack of arcs indicates that the two variables areprobabilistically independent. That is, one has no effect on the other.If birds fly regardless of the state of the stock market, there will beno link between “Birds Fly” and “Stock Market”. We can also say that twonodes, which have one or more common parents but no link between them,are conditionally independent. Finally, a node is conditionallyindependent of its indirect predecessors given its immediatepredecessors.

[0175] 3. Each node has a conditional probability table that quantifiesthe effects of the parents on the children. For example, given that abird is flying, what is the probability that it is injured? Nodes withno parents have a prior probability table.

[0176] 4. There may be no directed cycles in the belief network.

[0177] A BBN model enables the probabilistic classification of assetfault events on the basis of the probabilities of other observableevents. The belief network specifically enables expert judgment to beformalized so that one can specify a series of links of the form the‘truth of statement A supports my belief in statement B’, and can alsospecify how much the truth of A strengthens this belief in B compared,for example, to how much some other truth C would weaken this belief inB. This information is described mathematically by giving theprobabilities of the states of A conditional on those of B, orvice-versa, or via correlation coefficients between the states of A andB.

[0178] Bayesian belief networks are mathematically based on the scienceof probability. Bayes' law is based on the product rule which statesthat the probability of A and B is equal to the probability of A given Btimes the probability of B:

P(A{circumflex over ( )}B)=P(A|B)P(B)   (12)

[0179] similarly,

P(A{circumflex over ( )}B)=P(B|A) P(A)   (13)

[0180] Combining these two equations yields Bayes' law: $\begin{matrix}{{P\left( A \middle| B \right)} = \frac{{P\left( B \middle| A \right)}{P(A)}}{P(B)}} & (14)\end{matrix}$

[0181] In the equation above, A denotes a particular fault and B is afault indication event or alarm that has occurred. P(B) is theprobability of event B occurring, and P(B|A) is the probability of Boccurring given that A has already occurred. For example, given that asensor has failed (event A), P(B|A) is the probability that the signal'sfault detector will alarm (event B). P(B) is referred to as an a prioriprobability and P(B|A) is referred to as a conditional probability. Ifthese probabilities are known, then we can calculate the probability ofA occurring given that B has occurred, P(A|B).

[0182] In general, the indication that an event has occurred hasmultiple pieces of evidence. In that case, it can also be shown that ifB is a set of events, B_(i) then $\begin{matrix}{{P\left( A \middle| B \right)} = {\alpha \quad {P(A)}{\prod\limits_{i\quad}^{\quad}\quad {P\left( B_{i} \middle| A \right)}}}} & (15)\end{matrix}$

[0183] where α is the normalizing constant.

[0184] When there are multiple levels to a belief net, we apply thissame formula recursively to the parents, as: $\begin{matrix}{{P\left( {x_{1},x_{2},\quad \ldots \quad,x_{n}} \right)} = {\prod\limits_{i\quad}^{\quad}\quad {P\left( x_{i} \middle| {\Pi \quad x_{i}} \right)}}} & (16)\end{matrix}$

[0185] where Πx_(i) is the parent set of x_(i).

[0186] The theory underlying the formulation and use of belief networksand related techniques for probabilistic reasoning is detailed in JudeaPearl, “Probabilistic Reasoning in Intelligent Systems: Networks ofPlausible Inference,” Morgan Kaufmann, 1988, the disclosure of which isincorporated herein by reference.

[0187] Limitations of Prior Fault Classification Procedures

[0188] Expert systems, belief networks and related techniques forprobabilistic reasoning have been of limited practical use in the fieldof asset surveillance and equipment condition monitoring. While manyauthors and practitioners suggest their value, deployments are limitedto cases where the asset behavior is steady and predictable. Themajority of such deployments use rule-based expert systems having simplebackward chaining schemes to classify faults on the basis of a limitednumber of observable data features.

[0189] Gross suggests the value of an AI (artificial intelligence)diagnostic/prognostic system in U.S. Pat. No. 5,764,509 and again inU.S. Pat. No. 6,181,975. However, Gross does not report a reduction topractice or state claims for such an AI diagnostic/prognostic techniquedespite the apparent desirability of doing so. Moreover, Gross does notconceive of the novelty described infra for operating mode partitioningof an AI diagnostic/prognostic system.

[0190] Takeuchi describes a rule-based diagnostic/prognostic system inU.S. Pat. No. 5,009,833. While Takeuchi claims operation of therule-base on either observed or simulated data, he does not conceive ofthe novelty of comparing the observed signals to estimated signals forthe purpose of fault detection nor does he conceive of the novelty ofusing the fault detection results as the observable inputs to his expertsystem. Moreover, Takeuchi does not conceive of the novelty describedinfra for operating mode partitioning of the rule-baseddiagnostic/prognostic system.

[0191] Gross describes a rule-based diagnostic system in U.S. Pat. Nos.5,223,207, 5,459,675 and 5,761,090. Gross describes a SPRT faultdetection means that operates on the residuals formed from redundantphysical sensor signals. The SPRT fault detection means provides theobservable inputs to his expert system. However, Gross does not conceiveof the novelty of comparing the observed signals to estimated signalsfor the purpose of fault detection. Moreover, Gross does not conceive ofthe novelty described infra for operating mode partitioning of the faultdetection means or of the rule-based diagnostic system

[0192] In U.S. Pat. No. 5,274,572, O'Neill describes a blackboard expertsystem for asset surveillance but does not conceive of the noveltydescribed infra for operating mode partitioning of the blackboard expertsystem. In U.S. Pat. No. 5,392,320, Chao describes a rule-based expertsystem for asset surveillance but does not conceive of the noveltydescribed infra for operating mode partitioning of the rule-based expertsystem. In U.S. Pat. No. 5,402,521, Niida describes a neural networksystem for asset surveillance but does not conceive of the noveltydescribed infra for operating mode partitioning of the neural networksystem. In U.S. Pat. No. 5,465,321, Smyth describes a hidden Markovmodel system for asset surveillance but does not conceive of the noveltydescribed infra for operating mode partitioning of the hidden Markovmodel. In U.S. Pat. No. 5,680,409, Qin describes a principal componentanalysis system for asset surveillance but does not conceive of thenovelty described infra for operating mode partitioning of the principalcomponent analysis model.

[0193] Heger describes a Bayesian belief network (BBN) system for assetsurveillance in U.S. Pat. No. 6,415,276 but does not conceive of thenovelty described infra for operating mode partitioning. U.S. Pat. No.6,415,276 provides a discussion of the use of belief networks for faultclassification and is incorporated in its entirety herein by reference.

[0194] Novel Improvements to the BBN Fault Classification Procedure

[0195] Each of the fault classification procedures described in thesection above might be improved by the novelty described infra foroperating mode partitioning. Fault classification is improved byoperating mode partitioning because operating mode partitioning allows aunique and possibly different set of faults to be classified within eachoperating mode and further allows the signature and probability of eachfault type to be set differently within each operating mode. Forexample, faults having low probability of occurrence in one operatingmode and a high probability of occurrence in another operating mode aremore effectively classified separately. Further, the fault indicationpatterns that are indicative of an unacceptable asset condition might beset differently in one operating mode than in another (see for example,FIG. 7).

[0196] It is noteworthy that none of the practitioners enumerated aboveconceive of a method or system consisting of a unique combination of thefollowing three elements:

[0197] 1. A parameter estimation method and system for producing acurrent estimate of signal data values correlative to an observation ofsignal data values acquired from an asset;

[0198] 2. A fault detection method and system for comparing one or moreof the current estimates of signal data values to the correspondingobserved signal data values acquired from an asset to produce faultindications; and

[0199] 3. A fault classification method and system for determining thepresence, cause and/or explanation of an unacceptable asset status orcondition based on the fault indication results.

[0200] Moreover, it is noteworthy that none of these practitionersconceives of any method or system that further includes the fourthelement as follows:

[0201] 4. An operating mode partitioning method and system for providingmore accurate and efficient performance of any of the three elementsenumerated above by partitioning the characteristics of at least one ofthe three elements according to the expected operating modes of theasset.

[0202] Neural Network Method and System for Determining Operating Modeof Asset:

[0203] A method to determine the operating mode of the asset 12 isrequired for both the training procedure 20 and the surveillanceprocedure 60 using an operating mode partitioned decision model 50. Foreach new data observation, the operating mode determination procedure 26must classify the observation as belonging to exactly one of a pluralityof defined operating modes thereby allowing the required decisionsubmodel to be selected for training or surveillance. The operating modedetermination procedure 26 may use any form of algorithm that candetermine the current operating mode of the asset 12 based on one ormore data observations from the asset. The specific implementation ortype of the operating mode determination procedure 26 does not affect ormodify the operation of the instant invention.

[0204] In an embodiment of the invention, a Learning Vector Quantization(LVQ) neural network is used for the operating mode determinationprocedure 26. The LVQ neural network procedure is generally applicableto a wide range of assets. An LVQ neural network model is created for aspecific asset using conventional neural network training algorithms tolearn the inherent operating mode relationships within a set ofhistorical process operating data. The trained LVQ model is then used toperform the operating mode determination procedure when presented witheach new data observation. Because the LVQ neural network is trained bypattern matching a vector of observations from historical data, thistype of neural network will always determine the most similar operatingmode when presented with a new data observation.

[0205] An LVQ neural network is a two-layer, pattern classificationneural network in which each output node represents a particular classor category.

[0206]FIG. 16 illustrates the architecture of an LVQ neural network. AnLVQ network is one of a group of related pattern classification neuralnetwork models that can be used to cluster a set of s-element inputvectors {X}={x₁, . . . , x_(i), . . . , x_(s)} into t clusters. Theinput nodes of the neural network draw data either directly from sensorsignals or from the output of a mathematical function applied to one ormore sensor signals. An input vector is defined as the set of datavalues, one value for each input node that is derived from the sensorsignals at a given moment in time. The output nodes of the networkcorrespond to one of the classes (herein, the operating modes)recognized by the neural network. During operation of the neuralnetwork, an input vector is presented to the network, passes through thenetwork, and activates one of the t output nodes (y₁, . . . , y_(j), . .. , y_(t)). Each of the output nodes corresponds to one of the classesrecognized by the neural network. The LVQ neural network returns theclass corresponding to the activated output node, thereby determiningthe current operating mode of the asset.

[0207] The input nodes are connected to the output nodes by a set ofconnection weights. The subset of connection weights that connect all ofthe input nodes to one of the output nodes is called a weight vector.For example, output node y_(j) is connected to the input nodes by weightvector {W_(j)}={w_(1,j), . . . , w_(i,j), . . . , w_(s,j)}. An LVQneural network that contains s input nodes and t output nodes wouldcontain a total of t weight vectors, with each weight vector containings connection weights.

[0208] An LVQ neural network is designed to recognize a predefined setof classes. Each one of the classes corresponds to a distinct operatingmode of the asset under surveillance. During training of an LVQ neuralnetwork, the designer decides how many output nodes will be used tomodel each of the operating modes classified by the network. More thanone output node can be used to represent a class (operating mode)recognized by the neural network. By using more than one node torepresent a class, the number of neural network connection weightsdedicated to that class is increased. This improves the ability of theneural network to recognize an operating mode of the asset. For each ofthe r classes, the designer specifies the number of output nodes thatwill model that class.

[0209] A supervised training scheme is used for training an LVQ neuralnetwork. In this scheme, training is accomplished by presenting asequence of matched pairs of input vectors and target vectors to theneural network, causing some of the network's connection weights to beadjusted with each presentation of a training pair. The target vector{T}={t₁, . . . , t_(i), . . . , t_(t)} is a set of binary values, onevalue for each output node in the network. An element of a target vectorhas a value of one if the corresponding output node represents thecorrect class for the input vector. Conversely, an element of a targetvector has a value of zero if the corresponding output node representsan incorrect class for the input vector.

[0210] For each training pair presented to the LVQ network, theEuclidean distance between the input vector and each of the weightvectors is calculated. The Euclidean distances are then ordered, fromsmallest to largest. Only the weight vectors that produce the smallesttwo distances in the ordered sequence are allowed to learn. This form oflearning is called competition, because only those weight vectors thatproduce the best scores (i.e., producing the minimum Euclideandistances) are modified during an iteration of the training algorithm.Three commonly used learning methods for training an LVQ neural networkare herein designated LVQ1, LVQ2.1, and LVQ3.

[0211] In the first learning method (LVQ1), only the weight vector thatis closest to the current input vector (i.e., the weight vector thatproduces the minimum Euclidean distance) is allowed to learn. For eachmatched pair of input and training vectors presented to an LVQ networkduring training, the Euclidean distance between the input vector andeach of the weight vectors is calculated and the output node connectedto the weight vector that produces the minimum Euclidean distance isidentified. If the output node that produces the minimum Euclideandistance corresponds to the correct operating mode, the connectionweights for the output vector are positively reinforced as follows. Letthe subscript j represent the output node whose weight vector producesthe minimum Euclidean distance. If the target value for that output nodeis 1 (i.e., t_(j)=1), then the weight vector for the output node (W_(j))is updated by

{right arrow over (W)} _(j) ={right arrow over (W)} _(j)+λ({right arrowover (X)}−{right arrow over (W)} _(j))   (17)

[0212] where X is the current input vector and λ is a scalar parametercalled the learning rate that varies from 0 to 1. If the output nodewhose weight vector produces the minimum Euclidean distance correspondsto the incorrect operating mode (i.e., t_(j)=0), the connection weightsfor the output vector are negatively reinforced by

{right arrow over (W)} _(j) ={right arrow over (W)} _(j)−λ({right arrowover (X)}−{right arrow over (W)} _(j))   (18)

[0213] In the second (LVQ2.1) and third (LVQ3) learning methods, the twoweight vectors that are closest to the current input vector areidentified. These two weight vectors may be positively or negativelyreinforced depending upon a number of conditions. The most important ofthese conditions is that the two weight vectors are modified only ifthey are roughly equidistant from the input vector. A user-definedcontrol parameter (ε), called the window size, is used to determinewhether or not the two weight vectors are of comparable distances fromthe input vector. The window condition test that must be satisfied bythe two closest weight vectors is that the ratio of distance between theclosest weight vector and the input vector (d_(c1)) to the distancebetween the second closest weight vector and the input vector (d_(c2))must fall within the window. Namely, $\begin{matrix}{\frac{d_{c1}}{d_{c2}} > {1 - {ɛ\quad {and}\quad \frac{d_{c2}}{d_{c1}}}} < {1 + ɛ}} & (19)\end{matrix}$

[0214] The window size is a small user-defined constant with typicalvalues in the range 0.1<ε<0.5.

[0215] In the LVQ2.1 algorithm, a second condition that must be met isthat one of the two closest weight vectors connects to an output node ofthe same class as the input vector. While at the same time, the otherweight vector must connect to an output node of a class that differsfrom the class of the input vector. If both the window and classconditions are met, then the weight vector whose output node belongs tothe same class as the input vector is positively reinforced according toequation (17). Also, the weight vector whose output node belongs to aclass that differs from that of the input vector is negativelyreinforced according to equation (18).

[0216] In the LVQ3 algorithm, the two weight vectors that are closest tothe input vector are allowed to learn as long as the same window andclass conditions as in the LVQ2.1 algorithm are met. The LVQ3 algorithmcontains an additional learning mode. If the two weight vectors that areclosest to the input vector meet the window condition (i.e., theconditions in equation (19) are met), and if both weight vectors connectto output nodes that are of the same class as the input vector, thenboth weight vectors are positively reinforced. Both weight vectors areupdated by

{right arrow over (W)}={right arrow over (W)}+δλ({right arrow over(X)}−{right arrow over (W)})   (20)

[0217] where δ is a user-defined parameter, called the LVQ3 multiplier,that reduces the learning rate. The LVQ3 multiplier is a small constantwith typical values in the range 0.1<δ<0.5.

[0218] The concept behind the LVQ2.1 and LVQ3 learning methods is thatas the input vectors used for training are presented to the neuralnetwork, learning occurs only when an input vector is close to two ofthe weight vectors. In this case, the input vector is near the boundarybetween two weight vectors. Learning occurs in the LVQ2. 1 algorithmonly if one of the weight vectors belongs to the same output class asthe input vector and the other weight vector belongs to a differentclass. The weight vector belonging to the correct class is positivelyreinforced and the other vector is negatively reinforced. The LVQ3algorithm contains the same conditions as the LVQ2.1 algorithm. But anadditional condition in the LVQ3 algorithm allows the network to learn,at a slower rate, if both weight vectors belong to the same class as theinput vector. Over the course of the iterative training procedure, thistechnique works to sharply define the boundaries between the vectorspaces recognized by each weight vector.

[0219] A set of input vectors and corresponding target vectors are usedto train the LVQ neural network. The set of input and target vectors ispresented to the network and the connection weights are adjusteddepending upon the learning algorithm selected. Then, the learning rateparameter (λ) is decreased by a small amount and the set of input andtarget vectors is passed through the network again. The cycle isrepeated until the learning rate decreases to zero or until the errorrate for the neural network converges. Each training cycle of datapresentation and learning rate reduction is called an epoch. The maximumnumber of epochs (n_(eps)) to be performed by the training algorithm isa user-defined control parameter. The learning rate decreases linearlywith epoch number, with the learning rate decreasing to zero when themaximum number of epochs is reached. The initial value of the learningrate (λ₀) is a user-defined control parameter that, along with themaximum the number of epochs, determines the rate at which the learningrate is decreased. Specifically, the learning rate is decreased by afactor of n_(eps)/λ₀ at the end of each epoch.

[0220] During each training epoch, the error rate for the neural networkis calculated. The error rate is defined to be the fraction of inputvectors that are incorrectly classified by the neural network. An inputvector is correctly classified if the weight vector that is closest toit connects to an output node of the same class as the input vector. Aseach input vector in the training set is passed through the LVQ neuralnetwork during a training epoch, the program notes if the input vectorwas correctly or incorrectly classified. The error rate is then given bythe ratio of the number of incorrectly classified input vectors to thetotal number of input vectors in the training set. By keeping track ofthe error rate, the training algorithm can be halted as soon as theneural network stops learning.

[0221] The learning methods devised for the LVQ neural network arefine-tuning procedures. Only slight modifications are made to thenetwork weight vectors during any training epoch. Therefore to minimizethe number of epochs needed to train the neural network, the initialvalues of the weight vectors must be chosen wisely. The simplest methodof initializing the weight vectors is to randomly select t vectors fromthe set of input vectors used to train the neural network and use themas initial values for the weight vectors, where t is the number ofoutput nodes in the network. Although this initialization method works,a better method, which in general reduces the number of epochs needed toadequately train the network is to use the K-means clustering algorithmto set the initial values of the weight vectors. The K-means clusteringalgorithm is a method that will divide a vector space into K clustersand identify the centers of each cluster. The K-means clusteringalgorithm can be used to divide the input vectors used to train the LVQnetwork into t clusters and use the centers of the clusters as theinitial values for the weight vectors.

[0222] The K-mean clustering algorithm is used to initialize the weightvectors as follows. For each of the r classes recognized by the network,the input vectors that belong to each class are identified and collectedinto r arrays. Next the output nodes that belong to each class areidentified. By definition, the number of output nodes that belong toeach class is given by the nodes-per-class vector (N_(class)). Then foreach class, the K-means clustering algorithm is used to cluster theinput vectors that belong to the class into a number of clusters thatequals the number of output nodes that belong to the class. For instancefor class j, the K-means clustering algorithm is used to divide theinput vectors into nout_(j) clusters and to evaluate the centers of theclusters. The cluster centers for class j are used to initialize theweight vectors whose output nodes belong to the class. The K-meansclustering algorithm evaluates cluster centers for the class byminimizing the Euclidean distances between each of the input vectors inthe class and the cluster center nearest to each. Thus, each clustercenter is the mean value of the group of input vectors in a clusterdomain. The K-means clustering algorithm was found to improve the recallcapabilities of the neural network over the random initializationscheme, at a minimal increase in the computational cost of the trainingcalculations.

[0223] A trained LVQ neural network operates as follows. At a point intime, a current data observation is acquired from the asset 12 and aninput vector is constructed. The Euclidean distance between the inputvector and each of the weight vectors is calculated. The weight vectorproducing the minimum Euclidean distance is found and its correspondingoutput node is activated. The neural network declares the operating modecorresponding to the activated output node to be the current operatingmode of the asset 12 under surveillance.

[0224] In Use and In Operation Using A MSET Parameter Estimation Modeland A Neural Network for Determining the Operating Mode of the Asset

[0225] Operating mode partitioned decision processing was first reducedto practice by applicant in the performance of NASA Contract NAS4-99012.Testing performed under this contract conclusively demonstrated thereduction to practice for and unobvious benefits of the instantinvention. The contract final report and new technology disclosuredocuments by applicant, delivered to the United States Government underthis contract and listed herein below, further describe one embodimentand its reduction to practice, the disclosure of which is incorporatedin its entirety herein by reference.

[0226] NASA SBIR Phase I Final Report, “System State Determination forReal-Time Sensor Validation,” NASA Contract NAS4-99012, Jun. 12, 1999.Publication or disclosure restricted to US Government personnel for fouryears pursuant to Code of Federal Regulations 48 CFR 52.227-20.

[0227] New Technology Report for NASA Contract NAS4-99012, “PhasePartitioning the Multivariate State Estimation Technique (MSET) Processfor Improved Parameter Estimation Performance and Processing Speed,”Expert Microsystems, Inc. Document Control Number 2000-4446, Jan. 24,2000. Publication or disclosure restricted to US Government personnelfor four years pursuant to Code of Federal Regulations 48 CFR 52.227-20.

[0228] New Technology Report for NASA Contract NAS4-99012, “System StateClassification Using A Learning Vector Quantization (LVQ) NeuralNetwork,” Expert Microsystems, Inc. Document Control Number 2000-4447,Jan. 24, 200. Publication or disclosure restricted to US Governmentpersonnel for four years pursuant to Code of Federal Regulations 48 CFR52.227-20.

[0229] In the performance of NASA Contract NAS4-99012, a sensorvalidation software module was designed to validate seventeen (17)mission critical telemetry signals for the Space Shuttle Main Engine(SSME), as listed in FIG. 18. These signals were selected based on theirimportance for real-time telemetry monitoring of the three Space ShuttleMain Engines during vehicle ascent to orbit. The names listed in FIG. 18use standard SSME nomenclature. Data from ten nominal Space Shuttleflights, with flights and engine positions as listed in FIG. 19, wereselected as the training data for the MSET submodels and LVQ neuralnetwork used in the performance of this work.

[0230] A series of parametric studies were performed to determine theLVQ neural network configuration and training constants that provide thebest performance for SSME operating mode determination. The neuralnetwork configuration and training constants selected for applicant'sreduction to practice are defined in FIG. 17. Ten SSME flight data sets,defined in FIG. 19, were used to train the neural network. The operatingmode determination capability of the LVQ neural network was shown to beexcellent with operating mode classification error rates of less than 2%observed in testing with additional SSME flight data sets that were notused for training the neural network. Specifically, FIG. 20 illustratesthree versions of the sensor validation software module. The firstsensor validation software module, herein denoted the PD module, wascreated by the methods of the instant invention with a process memorymatrix (D) size of 150 vectors for each operating mode partitioned MSETsubmodel in the MSET decision model. The PD module's MSET decisionsubmodels were created using an LVQ neural network for the operatingmode determination procedure. The second sensor validation softwaremodule, herein denoted the A150 module, was created by the unpartitionedMSET model creation procedure with a process memory matrix (D) size of150 vectors used in the unpartitioned MSET model. This enabled a directcomparison of surveillance performance between the operating modepartitioned (instant invention) and unpartitioned models given aconstant processing time. The third sensor validation software module,herein denoted the A300 module, was created by the unpartitioned MSETmodel creation procedure with a process memory matrix (D) size of 300vectors used in the unpartitioned MSET model. The A300 module enabledimproved surveillance performance for the unpartitioned MSET model case,albeit at the cost of greater processing time.

[0231]FIG. 20 further lists the parameter estimation model and faultdetector configurations used for feasibility testing.

[0232] The operating mode partitioned sensor validation module (denotedPD) incorporated an MSET decision model partitioned into seven (7) modesrepresentative of the primary operating modes of the SSME. The rulesused for partitioning the training data for the SSME operating modes areprovided in FIG. 21. The two unpartitioned sensor validation modules(denoted A150 and A300) were prepared using exactly the same trainingdata without the benefit of operating mode partitioning.

[0233] The Argonne National Laboratory System State Analyzer (SSA) typepattern recognition operator was used in all of the MSET models. Thefault detection models were all based on the SPRT mean positive and meannegative test methods. SPRT is a statistically derived test statisticwith an explicit, non-zero false alarm probability. For this reason,SPRT fault detectors are generally used in combination with amulti-cycle fault decision algorithm to filter out the possibleone-cycle SPRT alarms. The fault decision procedure was configured usinga four (4) out of seven (7) multi-cycle decision algorithm. This faultdecision procedure will declare a sensor failure whenever any 4 of thelast 7 observation cycles produce any type of one-cycle SPRT faultdetection alarm.

[0234] Performance testing clearly demonstrated the feasibility andbenefits of using the operating mode partitioned MSET decision model forreal-time sensor signal validation. Metrics used to evaluate the testresults included the following:

[0235] Total One Cycle Alarm Count—This is a measure of the total numberof SPRT fault detector generated alarms for a single simulation run. Fornominal cases, this is expected to be a near zero number. For failuresimulation cases, the number will be non-zero. This metric provides ameasure of the overall performance of the fault detection procedure.

[0236] Average Parameter Estimation Error Percentage—This is a measureof the globally averaged parameter estimation error. The global averagederror is the sum of the single cycle error for all sensors and dataobservations divided by the total number of sensors and dataobservations. This metric provides a measure of the overall performanceof the parameter estimation procedure.

[0237] Average One Cycle Processing Time—This is a measure of theglobally averaged single cycle validation processing time. The one cycleprocessing time is the sum of the processing time for all validated dataobservations divided by the total number of validated data observations.The processing time is calculated as the elapsed time between the timeof the test driver's call to the sensor validation module's surveillanceprocedure and the time that the surveillance procedure returns itsresults to the test driver.

[0238] Time to Failure Detection (Failure Simulations Only)—This is ameasure of the elapsed time between the first observation containingsensor failure data and the observation for which the sensor validationmodule declares the sensor failed. Time to fault detection depends onthe diagnostic capability of the sensor validation module, the time offailure occurrence and the nature and magnitude of the sensor failure.The data herein report the elapsed mission time between the initiationof a slow drift in the signal and the time that the drift failure wasdetected. For consistency, all test cases herein used a drift magnitudeof 0.2% of the nominal, full power level value of the sensor signalapplied per second of engine operating time.

[0239] Signal Error at Failure Detection (Failure Simulations Only)—Thisis a measure of the total accumulated drift error in a sensor signal atthe time of failure detection. The data reported herein normalize theerror at the time of detection in terms of a percentage of the nominal,full power level value of the sensor signal.

[0240] The results tabulated in FIGS. 22 through 27 demonstrate the verysignificant improvement in sensor validation performance achieved usingthe operating mode partitioned MSET decision model in accordance withthe instant invention. The operating mode partitioned MSET decisionmodel provided better fault detection sensitivity, lower parameterestimation error, and much faster processing time in comparison to theunpartitioned MSET models. The operating mode partitioned MSET decisionmodel exhibited zero (0) false alarms and zero (0) missed alarms duringall testing performed. The results tabulated in FIGS. 22 and 23 weregenerated using an LVQ neural network for the operating modedetermination procedure.

[0241] Two test series were performed for comparison of the operatingmode partitioned sensor validation module to the unpartitioned modules.In the first series, an unpartitioned model with a process memory matrixof 300 vectors was constructed (denoted A300). The operating modepartitioned model (denoted PD) used a process memory matrix of 150vectors for each individual operating mode. When compared to the 300vector unpartitioned model, the operating mode partitioned MSET decisionmodel in accordance with the instant invention demonstrated:

[0242] 34% reduction in parameter estimation error;

[0243] 73% reduction in per cycle processing time;

[0244] 73% reduction in time to detect a sensor signal drift;

[0245] 73% reduction in the total signal error at drift failuredetection.

[0246] In addition, the 300 vector unpartitioned model missed two subtlenoise failures that were properly detected by the operating modepartitioned decision model in accordance with the instant invention.

[0247] In the second series, the operating mode partitioned decisionmodel was compared to an unpartitioned model of equivalent run-timespeed. To accomplish this, an unpartitioned model with a process memorymatrix of 150 vectors was constructed (denoted A150). When compared tothe 150 vector unpartitioned model, the operating mode partitioneddecision model in accordance with the instant invention demonstrated:

[0248] 42% reduction in parameter estimation error;

[0249] Equivalent per cycle processing time;

[0250] 77% reduction in time to detect a sensor signal drift;

[0251] 76% reduction in the total signal error at drift failuredetection.

[0252] In addition, the 150 vector unpartitioned model produced twosensor failure false alarms and missed one noise failure in cases thatwere properly detected by the operating mode partitioned decision modelin accordance with the instant invention.

[0253] The operating mode partitioned decision model provides betterfault detection sensitivity because the operating mode specific MSETdecision submodels are better able to estimate the current value of eachobserved parameter. This capability of the operating mode partitioneddecision model is demonstrated by the reduction achieved in theparameter estimation error. Reduced parameter estimation error allowsthe SPRT thresholds for the fault detection model to be set to lowervalues thereby making the fault detection model more sensitive to theearly indications of sensor failure (fewer missed alarms). Thisphenomenon proportionally reduces the time to drift failure detection asillustrated by comparison of the results reported in FIG. 23 to theresults reported in FIG. 25 and FIG. 27.

[0254] Parameter estimation error may be traded off against processingtime by increasing the number of vectors in the process memory matrix.As is evident by comparison of FIG. 24 and FIG. 26, doubling the processmemory matrix size increased the single cycle processing time by afactor of four (2²). Operating mode partitioning provides an effectivelylarger process memory matrix without the concomitant penalty inprocessing time. For example, the operating mode partitioned SSME sensorvalidation module (PD) includes seven active operating modes withprocess memory matrices sized at 150 vectors per mode. This provides aneffective process memory matrix size of 1050 vectors with processingspeed equivalent to a process memory matrix containing 150 vectors. Asingle unpartitioned model of equivalent accuracy would be 49 (7²) timesslower than the operating mode partitioned decision model.

[0255] Processing speed results demonstrated the real-time monitoringcapability of the operating mode partitioned decision model. Singleobservation processing times of 5-msec (200 samples/second) weredemonstrated with the seventeen (17) sensor SSME sensor validationmodule running on a 300-MHz Pentium II processor. It is reasonable toallocate between 2 and 50-msec per data cycle for sensor validationprocessing in SSME real-time control applications. The results of thistesting show these goals are only attainable with operating modepartitioning of the MSET model in accordance with the instant invention.The unobvious benefits of the instant invention are thereforedemonstrated by this reduction to practice.

[0256] Alternate Embodiment and In Use and Operation Using A MSETDecision Model for Parameter Estimation and A Rule-Based Logic Sequencefor Determining the Operating Mode of the Asset:

[0257] In another embodiment, the same MSET decision model methods andprocedures described hereinabove were used with a rule-based logicsequence for the operating mode determination procedure 26. A rule-basedmode determination procedure is generally specific to a single type ofasset and may be implemented in a plurality of forms. A rule-based modedetermination procedure may use expert system or procedural logicdepending on the nature and complexity of the operating modes of theasset. In one embodiment herein, procedural logic representing the rulesspecified in FIG. 21 for determining the operating mode of the SSME wasreduced to practice using C language procedural software as follows.-----------Begin Source Code Listing----------- /* SSME operating modedeterminer function */ /* Copyright 1999 by Expert Microsystems, Inc. *//* All Rights Reserved */ #define START_COMMAND 33024.0 #defineSHUTDOWN_COMMAND 35328.0 #define COMMAND_ISSUED(COMVAL,DATUM)((DATUM>(COMVAL - 1.0)) && (DATUM <(COMVAL + 1.0))) enum SSME_modesSSME_mode_determiner (double *data, enum Boolean initialize) { float pc;/* Combustion chamber pressure */ float vehcom; /* Vehicle command code*/ float compc; /* Commanded chamber pressure */ static floatlast_PL=0.0; static int cycles_in_start=0; static float last_compc=0.0;static enum SSME_modes last_state=PREFIRE; if(initialize) { last_PL =0.0; cycles_in_start = 0; last_compc = 0.0; last_state = PREFIRE; returnPREFIRE; }; pc = data[PID63]; /* Chamber pressure is PID63 */ vehcom =data[PID280]; /* Vehicle command is PID280 */ compc = data[P1D287]; /*Commanded chamber pressure is PID287 */ /* Take care of special casesfirst...*/ if (last_state == PREFIRE) { if (COMMAND_ISSUED(START_COMMAND, vehcom)) { /* If we're waiting for START and receiveSTART, then we're in TRANSIENT. */ last_state = START01; return START01;} else { /* Keep waiting. */ last_state = PREFIRE; return PREFIRE; } }else if (last_state == SHUTDOWN || COMMAND_ISSUED (SHUTDOWN_COMMAND,vehcom)) { /* Once SHUTDOWN is detected, stay in SHUTDOWN untilre-initialized. */ last_state = SHUTDOWN; return SHUTDOWN; }if(last_state==START01) { if(++cycles_in_start<25) { /* 0 to 1.0 sec */last_compc = compc; last_state = START01; return START01; } else {last_state = START12; return START12; } }; if(last_state==START12) {if(++cycles_in_start<50) { /* 1.0 to 2.0 sec*/ last_compc = compc;last_state = START12; return START12; } else { last_state = START24;return START24; }; }; if(last_state==START24) {if(++cycles_in_start<25*4) { /* 2.0 to 4.00 sec minimum */ last_compc =compc; last_state = START24; return START24; }; }; /* ELSE... mainstageoperation. */ if((last_state==STEADY_LOW || last_state==STEADY_FULL)&&fabs(compc-last_compc)<3.35) { last_PL = pc; last_compc = compc;if(compc <2500.0) { last_state = STEADY_LOW; return STEADY_LOW } else {last_state = STEADY_FULL; return STEADY_FULL } } else {/* In transient*/ if(fabs (compc - pc) <= (5 * 3.35)) { /* Transition to steady-state.*/ last_PL = pc; last_compc = compc; if(compc <2500.0) { last_stateSTEADY_LOW; return STEADY_LOW; } else { last_state = STEADY_FULL; returnSTEADY_FULL } } else if(last_state==START24) { last_PL = pc; last_compc= compc; return last_state } else if(compc>last_compc || pc<compc) {last_PL = pc; last_compc = compc; last_state = UPTHRUST; returnUPTHRUST; } else { last_PL = pc; last_compc = compc; last_state =DOWNTHRUST; return DOWNTHRUST; }; }; } -----------End Source CodeListing-----------

[0258] Reduction to practice and performance testing was accomplishedusing the MSET parameter estimation techniques and rule-based operatingmode determination procedure described hereinabove. Substantiallyidentical test results were achieved using the rule-based method and theLVQ neural network method for the operating mode determination procedure12. This was expected because both methods implemented the sameoperating mode determination criteria, as defined in FIG. 21, albeitusing very different means. Reduction to practice using both neuralnetwork and rule-based methods illustrates that the instant inventionmay employ any one of a plurality of operating mode determinationprocedures 26 to achieve the benefits described herein. These techniqueswere also demonstrated using mathematical models, as shown in FIG. 28.

[0259] In Use and In Operation Using A Bayesian Belief Network forClassifying the Condition of an Asset and A Rule-Based Logic Sequencefor Determining the Operating Mode of the Asset

[0260] In one application, the novel surveillance method and system ofthe instant invention that combines methods for parameter estimation,fault detection, and fault classification was used for determining thestatus of instrument and system assets in smart, autonomous sensors andcontrol components developed for the X-33 Single Stage to OrbitDemonstrator Hydrogen Detection System. The instant invention wasdemonstrated by applicant in the performance of this work under NASAContract NAS13-01001. Testing performed under this contract conclusivelydemonstrated the reduction to practice for and unobvious benefits of theinstant invention. The contract final report and new technologydisclosure documents by applicant, delivered to the United StatesGovernment under this contract and listed herein below, further describeone preferred embodiment and its reduction to practice, the disclosureof which is incorporated in its entirety herein by reference.

[0261] NASA SBIR Phase I Final Report, “Autonomous Control SystemComponents,” NASA Contract NAS13-01001, November 2001. Publication ordisclosure restricted to US Government personnel for four years pursuantto Code of Federal Regulations 48 CFR 52.227-20.

[0262] New Technology Report for NASA Contract NAS13-01001, “ASurveillance System and Method having Probabilistic Fault Detection andClassification,” Expert Microsystems, Inc. Document Control Number2001-4473, November 2001. Publication or disclosure restricted to USGovernment personnel for four years pursuant to Code of FederalRegulations 48 CFR 52.227-20.

[0263] In the performance of NASA Contract NAS13-01001, a test setup wasconfigured for exposing two redundant hydrogen sensor assemblies tovarying concentrations of H₂ gas. Each of these sensor assembliescontains two H₂ sensor elements (designated LR and HR), one temperaturesensor element (designated T), and one heater control element. The testsystem computer 44 used the data acquisition means 40 to monitor thesignal sources 42 consisting of the three sensor elements on each of thetwo sensors as shown in FIG. 29 and actuated the alarm 83 if hydrogenlevels exceeded safety limits. The decision model 50 was comprised of anMSET parameter estimation model 52, a SPRT fault detection model 54, anda BBN fault classification model 56. The MSET parameter estimation modeland SPRT fault detection models were used to provide alarm indicationsfor signals that were behaving abnormally. As each observation wasanalyzed, the SPRT returned an array of ones and zeros indicatingwhether each signal's reading was normal or abnormal. The Bayesianbelief network (BBN) used the SPRT output as positive findings todetermine the state for each of its alarm indication leaf nodes. The BBNused this state information to determine the probability of any of thespecified possible causes of the fault, thereby classifying the fault.

[0264] BBN Configuration

[0265] The BBN applies the output from each fault detector associatedwith one of its alarm indication leaf nodes as a positive finding forthe leaf node. The BBN combines this information from all of its leafnodes to determine the probability of any of the specified possiblecauses of the fault. The BBN fault classification submodel 57configuration and probability tables can be different in each operatingmode. However, in this case a similar BBN fault classification submodelconfiguration was used in each of the two system operating modes. Theconfiguration was as follows.

[0266] Six fault nodes were created to determine the specific sensorelement that had failed. These were designated HR1 element, LR1 element,T1 element, HR2 element, LR2 element, and T2 element. Each sensorelement node had two possible states, good (not faulty) and bad(faulty). Two additional fault nodes were created to determine whetherthe entire sensor had failed. These were named Sensor1 and Sensor2. Theyalso took on the states good and bad. If a node is in a good state, theimplication is that the associated asset item is operating acceptably.For each of the fault nodes, we assign a prior probability. This is theprobability that the proposition is true. For example, we believe thatSensor1 and Sensor2 are reliable under the test conditions, so webelieve they will behave correctly 95% of the time. This implies thatthey will behave incorrectly 5% of the time. This may be due to anynumber of reasons, e.g., not supplying power to the sensor, damage tothe sensor's cables, etc. The reasons for this failure are not ofinterest, only that each sensor can be expected to operate correctly 95%of the time. We believe the probability of a bad element to be greaterthat the probability of a bad sensor, so we assigned a 10% priorprobability to each of the elements failing. Conversely, there is 90%prior probability that each element has not failed.

[0267] Each of the alarm indication nodes is a leaf node. Each leaf nodeis a child of at least one parent, and a conditional probability isrequired for each of the combinations of parent states. That is, giventhe state of each of the parents, there is a probability that the alarmleaf node will be in either the “normal” or “abnormal” state. Each faultnode indicating a bad element has an associated alarm leaf node as achild. Each “bad sensor” node has three of the leaf nodes as children.Therefore, each leaf node has two parents. The conditional probabilitytables require one entry for each state of the node for each combinationof the node's parent's states. This yields a conditional probabilitytable for each node that requires 8 separate entries.

[0268] Conditional probabilities for the leaf nodes were divided intothree conditions. If all of the parent states were “good”, theprobability that each of the child nodes were in a normal state was veryhigh (95%). If either the associated sensor or the element were “bad”,the probability that the sensor was behaving abnormally was also high(95%). If both the sensor and the element were “bad” the probabilitythat the sensor was behaving abnormally was higher than if only one orthe other were “bad”, so a 99% probability of failure was assigned.

[0269] The MSET/SPRT submodel combination provides one example of ameans to perform parameter estimation and fault detection in order todetermine the state of the alarm indication leaf nodes. The faultdetectors each return a normal or abnormal value for their associatedalarm indication nodes every time an observation is processed. Each ofthese values is treated as a positive finding on the associated alarmnode. A positive finding is a value that may be applied with 100%certainty, or a probability of 1. That is, we are positive that thisalarm node is reporting a “normal” or “abnormal” condition.

[0270] The BBN fault classification submodel is diagrammed in FIG. 30.The complete set of prior probability and threshold values for the BBNnodes are listed in FIG. 31 for Sensor1 and FIG. 32 for Sensor2. Thethreshold values simply define the posterior probability value abovewhich the node will be considered to be in an unacceptable condition.When the posterior probability value for the node exceeds the threshold,the BBN will classify the node and thereby the associated asset item asfaulty.

[0271] Test Procedure and Results

[0272] The system was exposed to H₂ at 0%, 0.1%, 0.5%, 1.0%, 5.0%, 10.0%and 100.0% concentrations during normal operation. Training data wascollected during these exposures from each of these elements atone-second intervals. Test data was taken several months later at 3.57%H₂ and 0% H₂ using the same sensors and test configuration. Next, thesedata were adjusted to create additional sets of training and test datawherein the effect of hydrogen tank venting in the vicinity of thesensors was simulated. Tank venting has the effect of increasing thebackground hydrogen concentration in the vicinity of the sensors and cancause the hydrogen detection system to produce undesirable false alarmsor missed alarms.

[0273] An operating mode determination procedure 26 was used to classifyeach observation on the basis of the tank vent valve state. Theprocedure classified the operating mode as OPERATING whenever the tankvalve indication was less than 50% open and the operating mode asVENTING whenever the tank valve indication was more than 50% open.

[0274] Two MSET parameter estimation submodels 53 were trained on thetraining data, one for OPERATING and one for VENTING. Two SPRT faultdetection submodels 55 were calibrated for each included sensor on thebasis of their corresponding MSET submodel estimates taken over thetraining data for their respective operating modes. Each fault detectorreturned an array of ones and zeros indicating whether each sensorelement's reading was normal or abnormal.

[0275] Test results demonstrated that the BBN is effective fordiagnosing faults detected by the parameter estimation and faultdetection procedures. Nominal (OPERATING) test data consisted of dataobtained during exposure to 3.57% H₂, and during exposure to 0% H₂ usingthe sensors that had been previously used to acquire the training data.As expected, the system accurately predicted the data and generated noalarms.

[0276] In order to further test the operating mode partitioned BBN faultclassification model, we overlaid drift errors on selected signal data,forcing the MSET/SPRT algorithms to generate fault indications. Thesesimulated faults and their resulting fault classification probabilitiesare summarized in FIGS. 33 through 34. Beliefs (fault classificationprobabilities) are listed with the most probable causes at the top ofthe list. In each case, the operating mode partitioned BBN faultclassification model correctly diagnosed the cause of the faultindications. The OPERATING mode test data in FIG. 33 illustrates thatwhen faults occur on three of the sensor's elements, the most likelycause of failure is the failure of the entire sensor. When one or two ofthe sensor's elements are abnormal, the most likely cause is elementfailure. As expected, when more than one element fails on a sensor, theprobability of the sensor having failed is increased, but a much largerincrease occurs with the third element failing. The VENTING mode testdata in FIG. 34 illustrates the same results.

[0277] For comparison, a hydrogen detection system model was configuredand run over the same OPERATING and VENTING test data, but without thebenefit of the operating mode determination procedure 26. That is, thedecision model was trained and operated as a single mode unpartitionedmodel. In this case, performance was satisfactory for the OPERATING testdata, which yielded the same decision results as the partitioned modelas shown in FIG. 35. However, the unpartitioned model failed tocorrectly classify all of the simulated faults when processing theVENTING test data as shown in FIG. 36. In the VENTING test cases, thedrift error in signal L1 is not detected. This leads to a missed alarmfor L1 when the only fault occurs on L1 and to an incorrect diagnosis ofthe failure of sensor 1 when H1, L1, and T1 simultaneously drift high.

[0278] These comparative tests demonstrate the improvement in decisionaccuracy that results from the unique methods of the subject invention.

[0279] While an MSET parameter estimation procedure is described hereinabove, any type of parameter estimation procedure can be used with theinstant invention. The novelty described infra is not a modification orimprovement to the MSET procedure, but is rather a new means of usingany existing parameter estimation procedure so as to more effectivelyaccomplish the fault classification objective.

[0280] While a SPRT fault detection procedure is described herein above,any type of fault detection procedure can be used with the instantinvention. The novelty described infra is not a modification orimprovement to the SPRT procedure, but is rather a new means of usingany existing fault detection procedure so as to more effectivelyaccomplish the fault classification objective.

[0281] While a BBN fault classification procedure is described hereinabove, any type of fault classification procedure can be used with theinstant invention. The novelty described infra is not a modification orimprovement to the BBN procedure, but is rather a new means of using anyexisting fault classification procedure so as to more effectivelyaccomplish the fault classification objective.

[0282] Accordingly, in one aspect the present invention provides asurveillance system and method having fault classification and operatingmode partitioning.

[0283] In another aspect the present invention provides a system andmethod for performing high sensitivity surveillance of a wide variety ofassets including industrial, utility, business, medical, transportation,financial, and biological processes and apparatuses wherein such processand/or apparatus asset preferably has at least two distinct modes ofoperation.

[0284] In another aspect the present invention provides a system andmethod for determining the status of an asset.

[0285] In another aspect the present invention provides a system andmethod for performing control of an asset.

[0286] In another aspect the present invention provides a system andmethod which partitions a parameter estimation model for a processsurveillance scheme into two or more coordinated submodels eachproviding improved parameter estimation for a single operating mode orrelated subset of operating modes of the process.

[0287] In another aspect the present invention provides a system andmethod which partitions a fault detection model for a processsurveillance scheme into two or more coordinated submodels eachproviding improved fault detection for a single operating mode orrelated subset of operating modes of the process.

[0288] In another aspect the present invention provides a system andmethod which partitions a fault classification model for a processsurveillance scheme into two or more coordinated submodels eachproviding improved fault classification for a single operating mode orrelated subset of operating modes of the process.

[0289] In another aspect the present invention provides a system andmethod which partitions a parameter estimation model, a fault detectionmodel, and a fault classification model for a process surveillancescheme into two or more coordinated submodels together providingimproved diagnostic decision making for a single operating mode orrelated subset of operating modes of the process.

[0290] In another aspect the present invention provides a system andmethod which creates an improved parameter estimation model for aprocess surveillance scheme using recorded operating data for an assetto train a parameter estimation model.

[0291] In another aspect the present invention provides a system andmethod as characterized above which provides an improved system andmethod for surveillance of signal sources, detecting a fault or errorstate of the signal sources, and determining the cause of the fault orerror state of the signal sources enabling responsive action thereto.

[0292] In another aspect the present invention provides the system andmethod which provides an improved system and method for surveillance ofon-line, real-time signals, or off-line accumulated signal data.

[0293] In another aspect the present invention provides a system andmethod for generating an improved virtual signal estimate for at leastone process parameter given an observation of at least one actual signalfrom the asset.

[0294] In another aspect the present invention provides a system andmethod as characterized above which provides an improved system andmethod for ultra-sensitive analysis and modification of asset processesand apparatuses using at least one parameter estimation technique forthe generation of at least one virtual signal parameter.

[0295] In another aspect the present invention provides a system andmethod as characterized above which provides an improved system andmethod for ultra-sensitive analysis and modification of asset processesand apparatuses using at least one fault detection technique forcomparing at least one virtual signal parameter to at least one observedsignal parameter.

[0296] In another aspect the present invention provides a system andmethod as characterized above which provides an improved system andmethod for ultra-sensitive analysis and modification of asset processesand apparatuses using at least one fault detection technique forassessing at least one observed signal parameter.

[0297] In another aspect the present invention provides a system andmethod as characterized above which provides an improved system andmethod for ultra-sensitive analysis and modification of asset processesand apparatuses using at least one diagnostic decision making techniquefor assessing the status of the asset using at least one observed signalparameter.

[0298] In another aspect the present invention provides a system andmethod as characterized above which provides an improved system andmethod for ultra-sensitive analysis and modification of asset processesand apparatuses using at least one diagnostic decision making techniquefor assessing the status of the asset using at least one virtual signalparameter.

[0299] In another aspect the present invention provides a system andmethod as characterized above which provides an improved system andmethod for ultra-sensitive analysis and modification of asset processesand apparatuses wherein the diagnostic decision technique used forassessing the status of the asset is a Bayesian network.

[0300] In another aspect the present invention provides a system andmethod as characterized above which provides an improved system andmethod for ultra-sensitive analysis and modification of asset processesand apparatuses wherein the diagnostic decision technique used forassessing the status of the asset is an expert system or other rulebased system.

[0301] In another aspect the present invention provides a system andmethod as characterized above which provides an improved system andmethod for ultra-sensitive analysis and modification of asset processesand apparatuses wherein the diagnostic decision technique used forassessing the status of the asset is a neural network.

[0302] In another aspect the present invention provides a system andmethod to classify the operating mode of an asset wherein theclassification is performed using an expert system having any one of aplurality of structures, training procedures, and operating procedures.

[0303] In another aspect, the present invention provides a system andmethod to classify the operating mode of an asset wherein theclassification is performed using a neural network having any one of aplurality of structures, training procedures, and operating procedures.

[0304] In another embodiment of the invention, an asset surveillancesystem is comprised of: an operating mode partitioned faultclassification model 56 of an asset 12 comprised of a plurality of faultclassification submodels 57 each having an asset operating mode M_(i)associated thereto; a fault indication means 70 for determining one ormore fault indications given a set of observed asset signals from theasset 12; means for determining at least one operating mode M_(i) of theasset 12 for the set of observed asset signals; a first selection meansfor selecting at least one of the fault classification submodels 57 fromthe operating mode partitioned fault classification model 56 as afunction of at least the one determined operating mode M_(i) forproviding a fault classification of determined fault indications forperforming asset surveillance. The fault indication means furtherincludes an operating mode parameter estimation model 52 comprised of aplurality of parameter estimation submodels 53 each having an assetoperating mode M_(i) associated thereto and a second selection means forselecting at least one of the parameter estimation submodels 53 from theoperating mode partitioned parameter estimation model 52 as a functionof at least the one determined operating mode M_(i). The faultindication means further includes means for processing the observedasset signals as a function of at least the one selected parameterestimation submodel for defining parameter estimated data. Additionally,the fault indication means includes an operating mode partitioned faultdetection model 54 comprised of a plurality of fault detection submodels55 each having an asset operating mode M_(i) associated thereto.Furthermore, the fault indication means further includes a thirdselection means for selecting at least one of the fault detectionsubmodels 55 from the operating mode partitioned fault detection model54 as a function of at least the one determined operating mode M_(i).Moreover, the fault indication means further includes means forprocessing the observed asset signals as a function of at least the oneselected fault detection submodel 55 for determining the one or morefault indications used for providing the fault classification ofdetermined fault indications by the first selection means selecting atleast one of the fault classification submodels 57 from the operatingmode partitioned fault classification model 56 as a function of at leastthe one determined operating mode M_(i) for providing the faultclassification of determined fault indications for performing assetsurveillance.

[0305] In another embodiment of the invention, a method for determiningasset status includes the steps of creating 31 an operating modepartitioned fault classification model 56 comprised of a plurality offault classification submodels 57 each having an asset operating modeM_(i) associated thereto; acquiring 62 a set of observed signal datavalues from an asset; determining 70 at least one fault indication as afunction of the observed signal data values; determining 26 at least oneoperating mode M_(i) of the asset 12 for the set of observed assetsignals; selecting 76 at least one fault classification submodel 57 fromthe operating mode partitioned fault classification model 56 as afunction of at least the one determined operating mode M_(i), and usingat least the one fault indication and at least the one selected faultclassification submodel 57 for classifying faults 76 for performingasset surveillance.

[0306] In another embodiment of the invention, a method for determiningasset status includes the steps of partitioning a decision model 50 intoa plurality of partitions e.g., 52, 54, 56, each partition having anoperating mode M_(i) associated thereto: employing a plurality ofdifferent methods 53, 55, 57 from a plurality of parameter estimationmethods 52, a plurality of fault detection methods 54, and a pluralityof fault classification methods 56 for different partitions; determiningat least one operating mode M_(i) of an asset 12; selecting at least oneof the plurality of partitions as a function of the determined operatingmode for tailoring the plurality of parameter estimation methods 52, theplurality of fault detection methods 54, and the plurality of faultclassification methods 56 to asset surveillance as a function of the atleast one determined operating mode M_(i).

[0307] In another embodiment of the invention, a method for determiningasset status includes the steps of acquiring a set of observed signaldata values from an asset; producing a calculated set of estimatedsignal data values correlative to the set of observed signal data valuesacquired from the asset; comparing the set of observed signal datavalues to the calculated set of estimated signal data values;determining a presence of a disagreement between the set of observedsignal data values and the calculated set of estimated signal datavalues on the basis of the comparison step, and determining a cause of adetermined presence of disagreement between the set of observed signaldata values and the calculated set of estimated signal data values forperforming asset surveillance. The method further including the step ofusing a Bayesian Belief Network (BBN) fault classification method fordetermining a presence of an unacceptable asset status or faultcondition on the basis of a disagreement between the set of observedsignal data values and the calculated set of estimated signal datavalues derived from the comparison step. The method further includingthe step of performing asset control as a function of the classifiedasset status or fault condition.

[0308] In another embodiment of the invention, a method for determiningasset status includes the steps creating 30 a fault detection model 54comprised of a plurality of fault detection submodels 55 each having anoperating mode Mi associated thereto; creating 31 a fault classification56 model comprised of a plurality of fault classification submodels 57each having an operating mode M_(i) associated thereto; acquiring a setof observed signal data values from an asset 12; determining at leastone operating mode of the asset 12 for the set of observed signal datavalues; selecting 64 at least one fault detection submodel from thefault classification model as a function of at least the one determinedoperating mode M_(i); determining 70 at least one fault indication as afunction of the observed signal data values; selecting 64 at least onefault classification submodel 57 from the fault classification model 56as a function of at least the one determined operating mode M_(i), andusing at least the one fault indication and at least the one selectedfault classification submodel 57 for classifying faults 76 forperforming asset surveillance. The method further including the step ofcreating 29 a parameter estimation model 52 comprised of a plurality ofparameter estimation submodels 53 each correlative to at least onetraining data subset partitioned from an unpartitioned training data set24 and each having an operating mode M_(i) associated thereto andwherein the step of determining 70 at least one fault indication as afunction of the observed signal data values includes the step ofdetermining at least one fault indication as a function of both theestimated signal values and the observed signal data values.

[0309] In another embodiment of the invention, a system for determiningasset status is comprised of: a parameter estimation model 52 comprisedof a plurality of parameter estimation submodels 53 each correlative toat least one training data subset partitioned from an unpartitionedtraining data set 24 and each having an operating mode M_(i) associatedthereto; a fault detection model 54 comprised of a plurality of faultdetection submodels 55 each having an operating mode M_(i) associatedthereto; a fault classification 56 model comprised of a plurality offault classification submodels 57 each having an operating mode M_(i)associated thereto; means for acquiring a set of observed signal datavalues from an asset 12; means for determining at least one operatingmode of the asset 12 for the set of observed signal data values; meansfor selecting 64 at least one parameter estimation submodel 53 from theparameter estimation model 52 as a function of at least the onedetermined operating mode M_(i); means for calculating a set ofestimated signal values from at least one selected parameter estimationsubmodel 53; means for selecting 64 at least one fault detectionsubmodel from the fault classification model as a function of at leastthe one determined operating mode M_(i); means for determining 70 atleast one fault indication as a function of both the estimated signalvalues and observed signal data values; means for selecting 64 at leastone fault classification submodel 57 from the fault classification model56 as a function of at least the one determined operating mode M_(i),and means for using at least the one fault indication and at least theone selected fault classification submodel 57 for classifying faults 76for performing asset surveillance.

[0310] Moreover, having thus described the invention, it should beapparent that numerous structural modifications and adaptations may beresorted to without departing from the scope and fair meaning of theinstant invention as set forth hereinabove and as described hereinbelowby the claims.

I claim:
 1. An asset surveillance system, comprising in combination: anoperating mode partitioned fault classification model of an assetcomprised of a plurality of fault classification submodels each havingan asset operating mode associated thereto; a fault indication means fordetermining one or more fault indications given a set of observed assetsignals from the asset; means for determining at least one operatingmode of the asset for the set of observed asset signals; a firstselection means for selecting at least one of the fault classificationsubmodels from the operating mode partitioned fault classification modelas a function of at least the one determined operating mode forproviding a fault classification of determined fault indications forperforming asset surveillance.
 2. The system of claim 1 wherein saidfault indication means further includes an operating mode partitionedparameter estimation model comprised of a plurality of parameterestimation submodels each having an asset operating mode associatedthereto and a second selection means for selecting at least one of theparameter estimation submodels from the operating mode partitionedparameter estimation model as a function of at least the one determinedoperating mode.
 3. The system of claim 2 wherein said fault indicationmeans further includes means for processing the observed asset signalsas a function of at least the one selected parameter estimation submodelfor defining parameter estimated data.
 4. The system of claim 3 whereinsaid fault indication means includes an operating mode partitioned faultdetection model comprised of a plurality of fault detection submodelseach having an asset operating mode associated thereto.
 5. The system ofclaim 4 wherein said fault indication means further includes a thirdselection means for selecting at least one of the fault detectionsubmodels from the operating mode partitioned fault detection model as afunction of at least the one determined operating mode.
 6. The system ofclaim 5 wherein said fault indication means further includes means forprocessing the parameter estimated data as a function of at least theone selected fault detection submodel for determining the one or morefault indications used for providing the fault classification ofdetermined fault indications by said first selection means selecting atleast one of the fault classification submodels from the operating modepartitioned fault classification model as a function of at least the onedetermined operating mode for providing the fault classification ofdetermined fault indications for performing asset surveillance.
 7. Thesystem of claim 1 wherein the fault classification of determined faultindications predicts asset failures.
 8. The system of claim 7 whereinthe fault classification of determined fault indications predictsspecific asset failures including one or more sensor failures.
 9. Thesystem of claim 7 wherein the fault classification of determined faultindications predicts specific asset failures including one or moreequipment failures.
 10. The system of claim 7 wherein the faultclassification of determined fault indications predicts specific assetfailures including an undesirable process operating condition.
 11. Thesystem of claim 1 further including means for performing asset controlas a function of the fault classification of determined faultindications.
 12. An asset surveillance method, the steps including:creating an operating mode partitioned fault classification modelcomprised of a plurality of fault classification submodels each havingan asset operating mode associated thereto acquiring a set of observedsignal data values from an asset; determining at least one faultindication as a function of the observed signal data values; determiningat least one operating mode of the asset for the set of observed assetsignals; selecting at least one fault classification submodel from theoperating mode partitioned fault classification model as a function ofat least the one determined operating mode, and using at least the onefault indication and at least the one selected fault classificationsubmodel for classifying faults for performing asset surveillance. 13.The method of claim 12 further including the step of performing assetcontrol as a function of the classified faults.
 14. The method of claim12 further including the step of predicting asset failures as a functionof the classified faults.
 15. The method of claim 12 further includingthe step of predicting asset failures including one or more sensorfailures.
 16. The method of claim 12 further including the step ofpredicting asset failures including one or more equipment failures. 17.The method of claim 12 further including the step of predicting assetfailures including an undesirable process operating condition.
 18. Anasset surveillance method, the steps including: partitioning a decisionmodel into a plurality of partitions, each partition having an operatingmode associated thereto: employing a plurality of different methods froma plurality of parameter estimation methods, a plurality of faultdetection methods, and a plurality of fault classification methods fordifferent partitions; determining at least one operating mode of anasset; selecting at least one the plurality of partitions as a functionof the determined operating mode for tailoring the plurality ofparameter estimation methods, the plurality of fault detection methods,and the plurality of fault classification methods for asset surveillanceas a function of the at least one determined operating mode.
 19. Themethod of claim 18 further including the step of performing assetcontrol as a function of the classified faults.
 20. The method of claim18 further including the step of predicting asset failures as a functionof the classified faults.
 21. The method of claim 18 further includingthe step of predicting asset failures including one or more sensorfailures.
 22. The method of claim 18 further including the step ofpredicting asset failures including one or more equipment failures. 23.The method of claim 18 further including the step of predicting assetfailures including an undesirable process operating condition.
 24. Anasset surveillance method, the steps including: acquiring a set ofobserved signal data values from an asset; producing a calculated set ofestimated signal data values correlative to the set of observed signaldata values acquired from the asset; comparing the set of observedsignal data values to the calculated set of estimated signal datavalues; determining a presence of a disagreement between the set ofobserved signal data values and the calculated set of estimated signaldata values on the basis of the comparison step, and determining a causeof a determined presence of disagreement between the set of observedsignal data values and the calculated set of estimated signal datavalues for performing asset surveillance.
 25. The method of claim 24further including the step of using a Bayesian Belief Network (BBN)fault classification method for determining a cause of a disagreementbetween the set of observed signal data values and the calculated set ofestimated signal data values on the basis of the comparison step. 26.The method of claim 24 further including the step of performing assetcontrol as a function of the classified faults.
 27. An assetsurveillance method, the steps including: a method for determining assetstatus includes the steps of: creating a fault detection model comprisedof a plurality of fault detection submodels each having an operatingmode associated thereto; creating a fault classification model comprisedof a plurality of fault classification submodels each having anoperating mode associated thereto; acquiring a set of observed signaldata values from an asset; determining at least one operating mode ofthe asset for the set of observed signal data values; selecting at leastone fault detection submodel from the fault detection model as afunction of at least the one determined operating mode; determining atleast one fault indication as a function of the observed signal datavalues; selecting at least one fault classification submodel from thefault classification model as a function of at least the one determinedoperating mode, and using at least the one fault indication and at leastthe one selected fault classification submodel for classifying faultsfor performing asset surveillance.
 28. The method of claim 27 furtherincluding the step of creating a parameter estimation model comprised ofa plurality of parameter estimation submodels each having an operatingmode associated thereto.
 29. The method of claim 28 further includingthe step of selecting at least one parameter estimation submodel fromthe parameter estimation model as a function of at least the onedetermined operating mode.
 30. The method of claim 29 further includingthe step of calculating a set of estimated signal values from at leastone selected parameter estimation submodel;
 31. The method of claim 30wherein the step of determining at least one fault indication as afunction of the observed signal data values includes the step ofdetermining at least one fault indication as a function of both theestimated signal values and the observed signal data values.
 32. Anasset surveillance system, comprising in combination: a parameterestimation model comprised of a plurality of parameter estimationsubmodels each having an operating mode associated thereto; a faultdetection model comprised of a plurality of fault detection submodelseach having an operating mode associated thereto; a fault classificationmodel comprised of a plurality of fault classification submodels eachhaving an operating mode associated thereto; means for acquiring a setof observed signal data values from an asset; means for determining atleast one operating mode of the asset for the set of observed signaldata values; means for selecting at least one parameter estimationsubmodel from the parameter estimation model as a function of at leastthe one determined operating mode; means for calculating a set ofestimated signal values from at least one selected parameter estimationsubmodel; means for selecting at least one fault detection submodel fromthe fault classification model as a function of at least the onedetermined operating mode; means for determining at least one faultindication as a function of the estimated signal values and observedsignal data values; means for selecting at least one faultclassification submodel from the fault classification model as afunction of at least the one determined operating mode, and means forusing at least the one fault indication and at least the one selectedfault classification submodel for classifying faults for performingasset surveillance.